{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "my_seed = 2017\n",
    "np.random.seed(my_seed)\n",
    "import random \n",
    "random.seed(my_seed)\n",
    "import tensorflow as tf\n",
    "tf.random.set_seed(my_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>link_id</th>\n",
       "      <th>throughput</th>\n",
       "      <th>delay</th>\n",
       "      <th>jitter</th>\n",
       "      <th>loss</th>\n",
       "      <th>入端口</th>\n",
       "      <th>出端口</th>\n",
       "      <th>组合</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00:56:56.600000</td>\n",
       "      <td>4-3-2-5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.355648</td>\n",
       "      <td>1.355648</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00:57:09.600000</td>\n",
       "      <td>4-3-2-5</td>\n",
       "      <td>3200</td>\n",
       "      <td>1.294017</td>\n",
       "      <td>0.061631</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00:57:36.700000</td>\n",
       "      <td>4-3-2-5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.390100</td>\n",
       "      <td>1.390100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00:57:49.700000</td>\n",
       "      <td>4-3-2-5</td>\n",
       "      <td>4930</td>\n",
       "      <td>1.552820</td>\n",
       "      <td>0.162721</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         timestamp  link_id  throughput     delay    jitter  loss  入端口  出端口  \\\n",
       "0  00:56:56.600000  4-3-2-5           0  1.355648  1.355648   0.0    4    5   \n",
       "1  00:57:09.600000  4-3-2-5        3200  1.294017  0.061631   0.0    4    5   \n",
       "2  00:57:36.700000  4-3-2-5           0  1.390100  1.390100   0.0    4    5   \n",
       "3  00:57:49.700000  4-3-2-5        4930  1.552820  0.162721   0.0    4    5   \n",
       "\n",
       "   组合  \n",
       "0  45  \n",
       "1  45  \n",
       "2  45  \n",
       "3  45  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#导入数据测试\n",
    "df2='45.xlsx'\n",
    "data2=pd.read_excel(df2)\n",
    "data2.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   0 3200    0 ...  672  672  720]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建一个Series对象\n",
    "s = data2[\"throughput\"]\n",
    "# 将Series转换为ndarray\n",
    "arr = s.to_numpy()\n",
    "# 输出数组\n",
    "print(arr)\n",
    "#检查数据类型\n",
    "type(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.rcParams['font.family'] = 'SimHei'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#all_data = np.fromfile(\"ec_data\")\n",
    "all_data=arr\n",
    "type(all_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "stand_scaler = MinMaxScaler()\n",
    "all_data = stand_scaler.fit_transform(all_data.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sequence_len = 10 #原来的\n",
    "sequence_len = 60 #最适合\n",
    "X = []\n",
    "Y = []\n",
    "for i in range(len(all_data)-sequence_len):\n",
    "    X.append(all_data[i:i+sequence_len])\n",
    "    Y.append(all_data[i+sequence_len])\n",
    "X = np.array(X)\n",
    "Y = np.array(Y)\n",
    "#这段代码的作用是将一个给定时间序列数据 `all_data` 按照指定的时间步长 `sequence_len` 进行滑窗切分，切分出一系列的样本数据，以便之后输入模型进行训练或预测。\n",
    "#具体来说，假设原始的时间序列数据为 $[x_1, x_2, ..., x_n]$，那么代码首先定义了两个空列表 `X` 和 `Y`，然后在一个 for 循环中对原始数据进行了以下处理：\n",
    "#1. 对于每个下标 `i`，从原始数据中切分出长度为 `sequence_len` 的子序列 `all_data[i:i+sequence_len]`，并将该子序列添加到 `X` 列表中；\n",
    "#2. 将切分后的子序列的下一个元素 `all_data[i+sequence_len]` 添加到 `Y` 列表中。\n",
    "#最终，`X` 和 `Y` 数组将分别包含所有通过滑窗切分得到的样本数据和对应的标签数据。其中，`X` 的形状为 `(n-sequence_len, sequence_len)`，\n",
    "#表示共有 `n-sequence_len` 个样本，每个样本有 `sequence_len` 个特征值；而 `Y` 的形状为 `(n-sequence_len,)`，表示每个样本对应一个标签（即下一个时间步的特征值）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(activation_name,):\n",
    "    model = Sequential()\n",
    "    #model.add(LSTM(128, input_shape=(sequence_len,1),return_sequences=True))  #原来的\n",
    "    #model.add(LSTM(128, input_shape=(sequence_len,1),return_sequences=True))\n",
    "    #model.add(LSTM(64))\n",
    "   # model.add(Dense(1,activation=activation_name))\n",
    "    #optimizer =tf.optimizers.Adam(learning_rate=0.1) #原来的\n",
    "    optimizer =tf.optimizers.Adam(learning_rate=0.05)\n",
    "    #model.compile(loss='mean_squared_error', optimizer=optimizer, metrics=['mape'])\n",
    "    model.add(keras.layers.LSTM(units=32, input_shape=(None, 1)))\n",
    "    model.add(keras.layers.Dense(units=1))\n",
    "    model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "    return model\n",
    "\n",
    "#原解析：\n",
    "#这些代码是用来构建一个神经网络模型，主要应用于时间序列数据的预测。具体作用如下：\n",
    "#导入Keras库中的Sequential顺序模型和LSTM层\n",
    "#定义了一个函数build_model，用于构建神经网络模型\n",
    "#在模型中添加了两个LSTM层，分别为含有128个节点的输入层和含有64个节点的输出层\n",
    "#向模型添加了一个具有1个节点的稠密层(Dense)，并指定激活函数为用户提供的activation_name参数（实现可配置）\n",
    "#定义了优化器Adam，并设置学习率为0.1\n",
    "#编译了模型，指定损失函数为均方差误差（mean_squared_error），优化器为Adam，并使用平均绝对百分比误差（mean absolute percentage error）进行模型评估\n",
    "#返回构建好的模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm = build_model(\"sigmoid\")\n",
    "step = 250"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1115, 60, 1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = lstm.fit(\n",
    "    X_train, Y_train, epochs=50, batch_size = 32,verbose=0,validation_data = (X_test, Y_test)\n",
    "    )\n",
    "\n",
    "#epochs (轮数): 整个训练集数据将被用来训练神经网络50次。\n",
    "#batch_size (批处理大小)：每个训练批次包含的样本数量为32。\n",
    "#verbose (冗长模式): 设置为0以关闭详细输出。\n",
    "#validation_data (验证数据): 将测试集作为验证数据传入，以了解模型的表现情况。\n",
    "#总体来说，lstm.fit()方法会根据提供的训练数据和超参数对模型进行训练，\n",
    "#并且返回一个 history 对象，其中包含一些有关模型表现的统计信息（例如每轮结束后的训练和验证损失、准确度等）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 8ms/step\n"
     ]
    }
   ],
   "source": [
    "Y_predict = lstm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59, 1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_predict.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59, 60, 1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_predict_real = stand_scaler.inverse_transform(Y_predict.reshape(-1,1))\n",
    "Y_test_real = stand_scaler.inverse_transform(Y_test.reshape(-1,1))\n",
    "#stand_scaler.inverse_transform()函数是用来将经过标准化的数据还原回其原始状态。\n",
    "#这里，Y_predict_real 和 Y_test_real就是通过逆向变换重新获得实际的数值的数组。\n",
    "#reshape()函数则是用来改变数据形状的，由于标准化后的数组是一个列向量，所以需要使用reshape(-1,1)将其转化为行向量。\n",
    "#reshape（行，列）可以根据指定的数值将数据转换为特定的行数和列数\n",
    "#这里的-1被理解为unspecified value，意思是未指定为给定的。如果我只需要特定的行数，列数多少我无所谓，我只需要指定行数，那么列数直接用-1代替就行了，\n",
    "#计算机帮我们算赢有多少列，反之亦然。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x224562c6af0>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 2))\n",
    "plt.plot(Y_predict_real/(1024*1024))\n",
    "plt.plot(Y_test_real/(1024*1024))\n",
    "#其中figsize参数用于设置图形窗口的大小。\n",
    "#对于Y_predict_real和Y_test_real进行归一化处理，将返回的结果转换为单位为MB（兆字节），即除以1024*1024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "根均方误差(RMSE)：0.09023123862370759\n",
      "平均绝对百分比误差(MAPE)：0.3395939304942712\n"
     ]
    }
   ],
   "source": [
    "def MAPE(true, pred):\n",
    "    diff = np.abs(np.array(true) - np.array(pred))\n",
    "    return np.mean(diff / true)\n",
    "def RMSE(predictions, targets):\n",
    "    return np.sqrt(((predictions - targets) ** 2).mean())\n",
    "\n",
    "print(f\"根均方误差(RMSE)：{RMSE(Y_predict_real/(1024*1024), Y_test_real/(1024*1024))}\")\n",
    "print(f\"平均绝对百分比误差(MAPE)：{MAPE(Y_predict, Y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "请输入需要预测的数量200\n"
     ]
    }
   ],
   "source": [
    "N=int(input(\"请输入需要预测的数量\"))\n",
    "#不能输入太多 200左右 预测效果还行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#all_data[-sequence_len:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "current_seq = all_data[-sequence_len:]  # 获取最后一次的输入序列\n",
    "prediction = []                          # 存放模型预测结果\n",
    "for i in range(N):\n",
    "    # 对当前序列进行预测并加入到结果数组中 \n",
    "    predicted_value = lstm.predict(current_seq.reshape(1, sequence_len, 1))\n",
    "    prediction.append(predicted_value[0,0])\n",
    "    # 将当前结果作为下一次的输入序列的一部分\n",
    "    a=current_seq[1:]\n",
    "    b=predicted_value\n",
    "    current_seq = np.concatenate([a,b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "未来200个时刻的预测值为：\n",
      " [[ 91124.445]\n",
      " [122071.42 ]\n",
      " [143775.52 ]\n",
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      " [ 29675.875]\n",
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      " [420436.97 ]\n",
      " [489820.4  ]\n",
      " [498076.97 ]\n",
      " [342024.5  ]\n",
      " [113006.16 ]\n",
      " [ 30440.531]\n",
      " [ 86002.1  ]\n",
      " [143097.12 ]\n",
      " [174410.16 ]\n",
      " [203035.55 ]\n",
      " [240161.48 ]\n",
      " [277346.28 ]\n",
      " [307267.72 ]\n",
      " [328228.9  ]\n",
      " [340307.44 ]\n",
      " [344086.53 ]\n",
      " [341820.16 ]\n",
      " [338188.1  ]]\n"
     ]
    }
   ],
   "source": [
    "prediction_real = stand_scaler.inverse_transform(np.array(prediction).reshape(-1,1))\n",
    "print(\"未来\"+str(N)+\"个时刻的预测值为：\\n\", prediction_real)  # 打印未来N个时刻的预测结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#改数据格式\n",
    "prediction_real_change=[]\n",
    "for j in prediction_real:\n",
    "    prediction_real_change.extend(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#保存数据\n",
    "data3=pd.DataFrame()\n",
    "data3['预测']=prediction_real_change\n",
    "data3.to_excel('预测结果.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22459492940>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3JLKqyYpEJo8gvdHVBBfGg29xHYl0uiwUW9MIt3vzbjbo0WA1knikEcb88SU3uc02QTwK0ppCdcb8ggN2/SLxQkCIrWVyPFJZXsnDIhZh1JdAs92EBqtx0V9vsBoRps4j4hZIPCKqllAig7f/3+P4rW+dw3Qohc+/YzuO/8kjAEAbKI0wP2ntZkfLkzs7kecZXr5OxdlaoHcmgvWtdlhN+ps+39gmTlwj8UgLXJsqlGUv4TwCaOKaFvDF0pgKJXHHIlOhAKDDZYE7QoKEFhj1xmEx6uam4N1Kq8NE4pEG4HmGcX9iSUFiLraWIPFIbcZ8t3ezNNoEoYJ6j9Tndt1UgDCMgwqziVsh8YioWt4Y8KJ3JoK/etcOHPkvD+FjB9ejwWqE02wgR4tG6JuNwmbSY1XTzR0FO1a6sKbZhl9eJfFIC1yfjrwlsgYAXY1WmA066j3SCNemImh1mNHhMr/l18RrjEqz1UcUWzd3Ohf99c4GC2LpHGJp6pJQmzF/HOta7ItGoQCh94g6j9RnJpJCOscvOlIcoM4jLTHuj0PHAaublnIeFcQjesmrOmO++KLTC0VcVgOV0BNvgcQjomoZcEeh13H44N2rYdTP/6fcaDciRG+fNEHfbASbO5xvWZhzHIendnbi5LCPLLEqE05mMRVKLioe6XQcumnimmYQy7JvnQoFAF1z4hE5j9Rm5DY9OgDmXC4UXVOfkdsU+wJAm9NM09Y0gOhmWWqjazPpYTLoKLamAUb9CaxqssFkWHyL2WAj8UgLpLJ5TIdTSzrEgHnnEfVTEQsh8YioWgbcMaxrscFsuDlq02wzIUAPJdVhjKF/NnpT39FCntzZiWye4Ui/R+EjIxbSNxMBgLdMWhPZ0GbHsDeu5CERi5DM5DHoiS7adwQIi7wGq5GcRxpgxBsTpkI1vnUqFIC5Em0qzVaXXJ7HDX/ituJRq8MMHzmPVEfsplrKecRxHFrsJnIeaYDlolCNVsElFqbYmqrcCAgvmtYtEQUFhM6jHM+QpCnWxAJIPCKqlkF3FJs73ipMNNpM5DzSAN5oGsFEdknxaFPh3NEGSl2ui+LRIs4jANjQ5sBEMIEULR5UZdATBc+EyOdSrGqyknikAUYLUQD9ElGozgZyHmmBqVASOZ4tKUgAgvMoms7R/U9lxv1xmA1Ld1MBQJONxCO1YYwJ97/bXFON5DzSBHOC7G1ja8K5ooQAsRASj4iqJJXNYzyQmBMgFtJMb580QV+hLHtL5+KbXbtJD44DopSnVpXemQha7Ca0O9/aowMAG9sdYGx+oUGogxgd3FiYgLcYgnhEsTW1GfHG0d229IJ8LrZGwrmqLBcvBITCbADUe6Qyo77EbbupgMLaj14cqkognkE0lbttFGq+MJsECTUZ9xchHlmEcxVJ0jqdmIfEI6IqGfLEwBiwueOtG6lGm5HeaGiA+UlrizuPOI6Dw2wg8Uhlrs8IZdmL9egAgvMIAPUeqcywJw69jrvtonxVkw2TwST1E6hINs/jRuD2USirSQ+XxUCuS5UZLcRxl+s8AkAT11RmzB+/bbwGEMQj6jxSlzG/8PJiqal4AGA16mHS62idrjKjvgSabMa5DqrFcFkNAIBIis4VMQ+JR0RVMugRhInFYmvNNhNi6RwyOV7pwyIW0DsbQbvTjKbCFJTFcFmMJB6pSC7PY8Adw7YViwt8gLCx4jhBvCDUY9gbw9rmpUtIAcF5lMjkEaRFuWpMBBLI8QzdbUs7xAAhukaxNXUZ88fhtBjmJnUtRqtDFI9IlFCLPM9ww5+4bbwQEMQjP4lHqiIWm9/uJQfHcWiwGanzSGXG/fFlr6l55xGtKYh5SDwiqpIBdwwGHbeo3bKxsBCk3iN16Z+NYusSPToiDrMBsTQ9lNRixBdHJscvWZYNCC6JrkYrOY9UZtgbW1aQWFUYjUzRNfUYKbhZbhdbA4TSbHIeqcuoL47uVvuSrktgXjyi2Jp6TIeSyOT5244UB4TOo2gqh2yeXhyqxbg/Dh0HrG66vUus0UoJAbUZ88VvG1kDhMJsgDqPiJsh8YioSgbdUaxvtS/6Fr7ZJohHlH1Xj1yex6AntmRkTcRpodiamgy4l3bwLWRjuwNDHhKP1CKX5zHmS2BD++0XequahOleVJqtHiM+4Tq5XY8OIPQeUeeRuixX7AsALYXOI4qtqceY//aT1kSa7cJGN0hrP9UY9SfQ1WS9rUMWoHoJtUll85gOp5YVj8TCbHIeEQsh8YioSgbcsSU3vE2F/G4wTjc7tRjzJ5DJ8diyjCjhIPFIVQbdMXDcfK/RUmxoc2DEFwPPU5eOGkwGhTfvy52nrjnxiJxHajHqi6PZbkKjbekoFCDE1rzRNHLkklCFVDaPqVByWUHCbNCjwWok8UhFxCjUckJfs11widHAFPUY9y/vZgGABquJCrNV5EZAWCMs1yPmtIidR7ROJ+Yh8YioOpKZPCaCCWxapCwbwFzHDr19Uo/+uUlryzmPjIil6aGkFkOeGNY022Ax6m/7+za0OZDK8pgOk6NFDcTI4HLikctihMtiIOeRigx748u6jgAhtsYz6tJRixuBBBhbXpAAhIlrFFtTj1FfAjaTfsmJoCJNBecRiUfqwBjDaBFRKEBwHoVpja4aoiC73Lky6nWwmfTkPCJugsQjouoY9oqT1pZyHpF4pDZ9sxHoddxtx4oDKExbo4eSWgx6oti0zDkCgA2F/pZhL5Vmq8G8eLT8olycuEaow4g3vmzfESDE1gBQdE0lRot0swBC7xE5j9RjzB/H2pbbd1MBmCs+J9e5OgTiGURTuWXdfECh84gECdWYi4IW5RIzUucRcRMkHhFVx3xPy+Kb3sZCbI3y1OrRNyt0Ui3naHFRbE01cnkeo744Nrbf3h0GABsKAtMw9R6pwpAnhlbH8lEoQOg9otiaOkRSWfhiaaxvXV6Q7WwoiEc0cU0VRPGomI1um9NMDjEVGfPFbzv6XUQUjwJxEvrUYMxfiEK1LH+uGm1GJDJ5pHN5uQ+LWIQxfwJNNiMaCvul2+GyGBGhl7zEAkg8IqqOAXcMRj235ChQi1EPm0lP1mUV6Z+NLhtZAwTnUTrHI5Oj3g+lGQ8kkM2zZd1hgFBCz3GgN4UqMeyNLztpTUR0HjFG/VRKM1rkpDVAiK0BoIlrKjHqjaPVYZ4bRX07Wh1m+Ci2pgq5PI8bgURRDgnRdR4g55EqjJUgyDYUzhU5WtRhzBdfcg91Ky6rAZEkveQl5iHxiKg6Bt1RdLc6YNQv/Z9vk81EsTWViKdzuBFIYOsyZdnAfBkf9R4pz6BbcBEVE1vT6TjYjHok6DwpDmMMQ57Ysn1HIquarEhk8giS81JxxElrxcQLW+wmGPUcxdZUYtRfnJsFEJxH0XQOqSy5JJRmKpREjmdFCRJGvQ5Oi4HWfiox7o9DxwGrm4pwHokj4Ok5pQrj/kRRkV2AnEfEWyHxiKg6BjzRJcuyRZrsRgTJeaQK/e7iyrIBwFF460u9R8oz5BHO04YixCMAsJkNiGdo86Q0gXgG4WS2KEECEMQjgCauqcGot7B5al5+86TTcWh3WuCm2JoqjPriRW+eWh2CS4JKs5WnlG4qQBBlyXWuDqP+BLqarDAZlt9aztVLkPNIcVLZPKbDSawtIl4IAC7qPCJugcQjoqpIZHKYCCSXLMsWEZxHdLNTA3HS2tZO17K/V3QeUe+R8gx5YuhqtMJhNhT1++0mPeLkPFIcsaS8WJFvVeGtL5VmK8+wL47VzTaYDbfvehPpcJnJeaQC0VQW3mhx3VSA4DwCQKXZKlDsVCiRJhKPVGPcX9ykNQBotAqCLHWTKs9ECZMmAaEwm6atEQsh8YioKoYKhb1LlWWLUGxNPfpno7Cb9HMOiNtB4pF6DHpiRQsSAGAzGZDI0HlSGnHS2sYiY2td5DxSjRFvHN1FLsgBoTSbxCPlGS8U+xYbW2t1COIROY+UZ8yfgMNsmHN/LUezjcQjNWCMYdRXgng0N9iGzpXSiG6+ojuPLAZE0znwPPUoEgIkHhFVxYDY07Ks84hia2rRNxvB5k4ndLrbj9UFAKdZWEBQ55Gy5HmhR6eYviMRu1mPeJpia0oz7InBbNChq3F5MRYQ3hJajXp4IrTRVRKeZ4WpUMVfUx0uiq2pwchcFKq4cyWKRzRxTXlGfXGsa7WB45ZfTwCC84heHCpPIJ5BNJUrqpsKwNyUL4pDKc+ceF50YbYRjAExenlIFCDxiKgqBt1RmPQ6rF2mU6LJbkIklUMuT1O8lIQxhv7ZKLYW0XcELHQe0QJCSaaCSaRzfEniETmP1GHYG0N3m6MoMVbEbtYjQeW+ijIbSSGZzRc1aU2k02VBPJOn+5/CjHrj4DgU3fnRUnC9UGxNecb8xU+FAoTOI388Q9MmFWasIEisK/KacpoN0Os4iq2pwKg/jkabcU7AWw5xIiWVmxMiJB4RVcWAO4ruNjsMt5m0BsyPbKUyPmXxRNMIJrLYUsSkNQBw0LQ1VRjyCr1UyxXPL8Ru1lNhtgoMe+NFl2WLWE16JOlcKcpIoZuqJPGowQIAcFN0TVHG/HGsbLDCYiyum8ps0KPBaqTYmsJk8zwmg8miHRKA8OIwk+ORoPufosx1UxXpPOI4Dg1WI0JJcokpTSndVIDgPAJAE9eIOUg8IqqKAXds2bJsQFhAAKDomsL0zYqT1pYvywao80gtBt1ij05xIh9QcB6RyKcoqWweE8EENhTZdyRiNxmo3FxhRn3CNdVdYmwNAGbDJEooyUgJk9ZEWh0mch4pzGQwiTzPihYkAKHzCAD1HinMuL8wabKpOOcRADRajeQ8UoExX6JohxgAuKzCOj2SpDUFIUDiEVE1xNM5TIWSy5ZlA0LnEQCauKYw/bMRACg6tmY26GHS60g8UphBTwxtTnPRtmWgMG2N3uYqypg/DsaKn7QmYjXpkaTYmqIMe+Owm/TocJmL/jOdonhEziPFYIxh1BsrQzwyk3ikMGNz3VTFb3Sb7SQeqcGoP4GuJitMhuK3lQ02GgGvNOFkFlOhJDaWsKYQY2vkPCJESDwiqoZBT3Fl2cB8bI0WEMrSNxtFh8s85/wqBqfFQJ0fCjNYYlk2ANjM1HmkNMMeYfNUamyNnEfKM+KLY32bvehiX4Bia2oQTGQRKaHYV6TNaabCbIURp0KVErER1x4BKs1WlFKjUAA5j9SgZyoMANi1qrHoP9MgxtZI6CMKkHhEVA0DbiESVUpsjcaAKkv/bLToyJqIw2Ig55GCMMYwXIZ4ZDfpkc0zZHJUQq8Uw97So1CA4Dyizg9lGfXFSj5PFqPQpUPikXLMxwtLdx5R55GyjPnjcFoMc26iYmimygLFYYwJU/FKFY9sJuo8UpgronjU1VD0nxE7j8glRoiQeERUDYPuKMwGHdYsM2kNWJB7J/FIMXJ5HoOeWNGRNRGnxUCF2QoyG0khls5hY5Gl5iI2k5B7J/eRcgx7Y+hqtMJqKq7YV8RO4pGipLJ5odi3REECKNz/SDxXjFFfYUx1Gc6jWDqHFMVBFWO00E1VipuPYmvKMxVKIprKlRSFAgRHCzmPlOXqZBirmqwlCbJOswEcB0ToOUUUIPGIqBoG3DFsaHNAX8TIaqtJD7NBRw8mBRnzx5HJ8UVPWhNxmCm2piTzZdklOo/MgoBBvUfKMeSJldx3BABWk4HEIwUZ9yfAWGmT1kRsJPQpyqgvBoOOw6oma0l/rtUhbLbIfaQcY2VEoVwWYQQ8iUfKcWkiBAC4Y3VjSX+u0WZENJVDLk9uZqW4MhXC7lXFu44AQKfj4DAbKLZGzEHiEVE1DLqjRZVlizTZTLSAUBBx0trWFaU6j4wUW1OQ+e6wEjuPROcRucQUgecZRrzxkvuOANF5ROdJKUYK8cJSp+IBBaGP3CyKMeqLY02zDQZ9acvfNqdQhO6l0mxFSOfymAomS+6m4jgOTTYTguQ6V4xLN0IwGXTYtqK0yoLGuRHw9KxSgmA8g4lAEru6Gkv+sy6LkQqziTlIPCKqgmgqi+lwqqiybJEmu4k6jxSkfzYKvY4r2brsNFPnkZIMeWJoshnRUoJtGSDnkdLMRFJIZvNlCRK2wrQ1nmcyHBlxKyNisW8ZsTWbUU+CrIKM+hJlnadWhyAe+ch5pAi9M1HwrPjJrQtpthvhp3Jzxbg0EcKOla6SJq0BQucRQN2kSnFtWug7KtV5BAi9R+Q8IkRIPCKqAtEtUUxZtkiTzYggxdYUo3cmivWtdpgNpfWzUOeRsgx5otjU7iypRwIg55HSDBfueaWKsYAwGY8xIJUjoU8JRrxxdLjMcJgNJf9Zu5lia0rB8wxjhR6dUpkTj0iUUITz40EAwF1rm0r+s812ch4pRTbP4+pUuOTIGgA02ATnUYhECUW4MimIRztXli4eNVgNiCRp7UcIFCUecRzXwXHcxcI/f53juFMcx312wa9L+hlB3ErfjBCJKqVPp8luookbCtLvjmBLGW8JHQXxiDFyScgNYwwD7hg2lhhZA4Tx7wA5j5RiuIIolK1QsE2ihDKMlDFpTcRqMiBJsTVFEN185TiPWgqdRz6KrSnC+fEAVjVZ0eGylPxnm+1UWaAU/bNRpHM89q4pXeQTY2thesmrCFcnw1jXYpsT7UqBYmvEQop1Hv0dACvHce8FoGeMHQDQzXHcJqk/k/6vSNQCVyZDaLQZsbq5+JJLwXlECwgliKVzmAgksbXEsmxA6DzK84w2UArgi2UQTmZLLssGAJtZFCTo7ZMSDHtjcFkMc0W9pTDvEqNrSglGfXGsL6ObCijE1uiaUoQLBTfLnjJiG2aDHg1WIxVmKwBjDOfHg9hXhusIQKHziDa6SnCxUJa9twzn0VxsLUnrdCW4OhXGrlWNZf1Ziq0RC1lWPOI47hEAcQCzAB4C8Gzhlw4DOCTDZwTxFi5PhrGrq6G0ka02E0LJLPLU+yE7A26xLLu0wkQAc1EP6j2Sn6Eyy7KBBc4jEiQUYdgTx4Z2R8nxQmCB8yhL15TcBOIZhBJZdJfhZgGEyaDkEFOGM6MB2E16bC/jOQUALeRoUYSpUBLuSLqsyBognKdgIkNrPwW4dCOEFrup5OmFwLzziKYiy48vlsZUKIndXaUL54DgPAqTeEQUuK14xHGcCcDnAPxJ4SM7gKnCPwcAdMjw2WLH8SmO485xHHfO6/UW+3cjaoRkJo8BdxR7SlTMG20mMAZSyxWgX5y0VkZszWkh8UgphjzCedrUXvp5IueRsgx7Y2VF1oB58YiEPvnpnYkAKK+bCiiUm2fyFNtVgDOjAdy1rrnkSWsiNjO5xJRA7Du6s1znkV1Y+9FmV34uTQRxx+rGsl5yuEg8UoyrU0Lf0a4yXJcA0GA1Ip7JI5fnpTwsokpZ7gn6JwD+kTEWKvx7DIAoLzsKf17qz94CY+wrjLF9jLF9bW1tRf3FiNrh+kwYeZ6VPCGguTBNKkDRNdnpm4nAbtKjq7H0t08ui7CAiFKeWnYGPTE4zQZ0uMwl/1lyHilHJJWFJ5quQDwSzlWSHC2yc2rYD72OK9slYTcbkOMZMrQol5VgPIN+dxT3rG8u+ztsRgO5xBTg/HgQdpMeWzvLc4jNrf3IJSYr4WQWw954WWXZAKDXcXBZDCTyKcDVyTA4DtixsrxrymWll7zEPMuJR48B+AzHcUcA3AHgHZiPlu0BMAbgvMSfEcRNXJ4QFPNSH1CN4iQHEo9kp282is2dTuh0pb99chScRzRxTX4G3bGyo1B6HQeLUUdv3hVAnLS2odweHdF5ROdKdk6N+LF7VQOcltJLSAHAahTOFQl98nJ2LAAA2F+BeGQ16ambTwHOjQWxd00T9GWsJwASj5TiymQIAHDHmsayv6PRZqI1ugJcmQyju9Ve9nNKfMlLpdkEANx2rixj7AHxnwsC0jsBHOM4biWApwDcC4BJ/BlB3MSVyRA6XRa0lzh1Y34BQTc7OWGMod8dxVM7O8v68xRbU44hbwwPbS7fvWk3GUiQUIAzo8JGd3eZ5ZaieESChLzE0zlcngjhUw90l/0dCyfjNdqkOjLiVs6MBmAy6Ep2MC/EZtJjKkTXlJzE0jn0zUbwu4+UPz+nyUbikRJcuhECUP5zChBe8obIeSQ7V6dCuG9Da9l/XowYkkuMAIqftgbG2EOMsQiEkuvTAB5mjIWl/kyqvxhRO1yeDJe14BMXEDRxTV480TRCiWzZFnOxMDtG4pGshBIZeKPpssqyRWxmPU3wUoDjQz5sanegs6H0MdWAEIUCyHkkN2fHAsjxrKJFuXWBeETIx5mxAPauboTZoC/7O6yFfipCPi7dCIFnKHvSGjD/4pDWfvJyaSKEDW12NFjLc7MAQpcOdR7JizuSgjuSxs4yy7IBwFV4yRtJ0pqCKEE8EmGMBRljzzLGZuX6jCBEwsksRn1x7CkjU90kLiDo7ZOs9BXKsreUUZYNYM5GS3ZYeZmbtFZGWbYIOY/kJ5XN48xoAAc3Vi5I0EZXXk4N+2HS68ruOwKon0oJYukcrk2FK+o7AgTnEcV25eX8eBAcV1kUimJr8sMYw6WJEO5YXf69DxBia+RmkZerk4IvoxLXZYON1unEPOWNnCAIhajkpmc36WHUcwjSWw1Z6StMGypn0how7zyi2Jq8nCtMsNleZmEiIG6eaJMrJ+fGgkjneNy/qXzxyGakaWtKcHLYjzvWNM6JdeVA/VTyc348CJ4B+9e3VPQ9NhMVZsvN+RtBbOlwznWslIPFqIfNpCfxSEYmg0n445mKRD4AaLQaqfNIZq5MhaHjgO0ryl/7zXUekdBHgMQjQuNcLhTy7e5qLPnPchyHRpuJnEcy0z8bRYfLjMZCTLBU9DoOdpOeCrNl5ki/B9tWuNBRYnfYQuxmA+J0nmTl+JAPBh2He7rL3+ga9DqYDDoksnSu5CKcyKJnOoz7NlQqSJBLTG7OjPph0HG4c21jRd9jNeqRzvHI80yaAyNuIs8zXBwPVuTkE2mitZ+sXJwIAQD2ljlpTaTRZkQ4mQVP15RsXJsKY2O7Yy7OXg5i5xE5jwiAxCNC41yZDGFdi23OMlkqzTYT5d5lpm82ii1l9h2JOCwGROmhJBuRVBbnxoJ4eEv5ZdkAOY+U4PiQF3euaZpz5JWL3UT9VHLy5qgfPAMOVCDyAfOxNbqu5OPMaAA7uxrm/n9dLnNCH01ck4VBTxTRdE4S8ajZbkKA1n6ycelGCGaDruy6ApEGqxE8A6L0UkoWGGO4MhnGrjJewC/EbtJDr+MoYkgAIPGI0DhXJsNl9R2JNNqMJB7JSC7PY8gTw7YKFxBOi5GcRzJyYtCHHM/w0Jb2ir6HOo/kJRDPoGc6gkMVRNZEKGIjLyeH/bAYdRXHNuanrdF1JQepbB6XJyrvOwLoXMnNuTEhWi2FeGQ368klKyOXJoLY1dUAo76ybaToWKfomjzMRlLwxdIV9R0BQpLDZTFQYTYBgMQjQsN4IinMhFMVjQFttpuo80hGRn1xZPJ8xW+fHGYDdR7JyOv9HjgtBtxZ6UaXpq3JyokhHxiDROIRlfvKyekRP+5e11zR9C5gQbk5uVlk4dJECJk8j/0SiEdWKjeXlQvjQbQ6zFjTbKv4u0g8l49Mjse16QjuqDCyBgidRwBo4ppMXCn0xu6qUDwChOgaxdYIgMQjQsNcLtz09lRw06POI3nprXDSmojTQuKRXDDG8Hq/Fw9sboOhwreE5DySl+ODPjgtBuyuYKSuCEUM5cMXS6NvNop7K4ysAQsKs0mUlYUzowFwHLBvrZTOIzpXcnD+RhB3rW0Ex3EVf5fVpCeRTyb6ZiPI5PiKXZeAkA4AgBDFoWTh6mQYeh1XUVm2iMtipMJsAgCJR4SGuTIZgl7HYcfK8jdSzXYjQsksGKMyPjl4o98Ll8WAzR1SiEf0UJKDnukIvNE0Hq4wsgYIb3NTWSqMlQPGGI4P+XDfhpaKRT5A2DyR80geTo/4AaDismwAsBj04DggSedKFs6MBrC101V2b+JCrCQeyYY3msa4PyGJyAcIEyfJzScPlwpl2ZI4j0TxiGJrsnBlKozNHU5YjJU5ZAHAZTVQ5xEBgMQjQsNcngxjU7ujojHITTYT8jxDhFwtkpPL83i1z41Ht3VUnHt3mqnzSC6O9HsAAA9urqwsGxB6JADq/JCDUV8cU6EkDm2q/DwBgkuMNrnycGrYD4fZgF0SOMR0Og5WI7nE5CCb53F+PChJ3xEgCBIAxdbk4Py40Hd0pwR9R4AontN5koNLN0JodZjR1Wit+LsarELnEYkS0sMYw9XJkCROZkAoN6e9FAGQeERoFGFCQAh7Kug7AgTxCABF12Tg7FgQoUQWb9veUfF3OSi2Jhuv93uxe1UD2pzmir+LJkPJx/EhHwDg/o2V9x0BtHmSk1PDfuxf3yyJQwwoRAzJJSE516bCSGbzkvQdAQvvf/Sskprz4wGYDDrs7Ko8XgNQbE1OLk2EcMdqaeKFDdR5JBtnx4IIJrK4W6L7H8XWCBESjwhNMhFIIpTIYvfqyhTzJrvwYKKJa9Jz+PosTAYdHpDA0eK0CC4JikNJSyiRwcUbwYqnrImIziOaYiM9xwZ9WNVkxdqWystiAdF5ROdJambDKYz44pJE1kRooysPZ0YDAIC710mzeaJyc/k4Px7E7q6GigvoRWxGAzJ5Hrk8L8n3EQK+WBojvjj2StB3BAAmgw52k57EIxn49ulxOC0GvH3XCkm+jwqzCRESjwhNcmkyBADSOY9IPJIUxhgO97hx/8ZW2M2Gir/PUfiOGLmPJOXooA88Ax7aIk0UipxH8pDL8zg97Mf9m1oleZsLFJxHVMIsOadGBIeYFGXZIjajgQRZGTgzGkB3m10S1yVAhdlykcrmcW0qgrskiqwBC84VCX2S8t3TNwAAT+yo3HEu0mgzIZSkNbqUeKIpvHhtBu+/a3VF1R8LcVmEzst0jq6peofEI0KTXJkIwWzQVTzFaz62Rmq5lPRMRzAVSuKJHZ2SfJ/LIjjEomk6T1JypM+DJpuxYhFWxG4i55EcXJ4MIZrO4dBGaUQ+QHCJJbJ5GhYgMSeH/GiwGiWZXiNiNVG5r9TkeYYzYwHsl8h1BJB4JBev9LqRyfOSCrJzLjE6V5KRyubx7dNjeHhLGza2V7Y2X0iD1YgwOY8k5QdnJpDNM/z6vWsk+05XIWIYSdL6r94h8YjQJFcmw9i+0lVxEXOTnZxHcnD4uhs6Dnh0mzRxKKdFcLRQ75F08DzDkQEvHtzcBr1OGjeLzUzOIzk4NugDx0kzvUvEZjIgzzNkKLYhKadG/DjQ3QKdRNcUUBD66JqSlKtTYURTOckia8BCQYKeU1LBGMOXXhvChja7JBF4ERL6pOenF6fgi2Xwyfu7Jf3eRpswFZmQhlyex7+duYH7N7Wiu80h2feK/VQUXSNIPCI0R55nuDYdlsQt4bIYoNdxJB5JzOGeWexb24wWhzRxAEdBPKKJa9JxZSqMQDyDh7dKI/ABC5xHtHmSlOODPuxc2TAndkvB3OaJomuSMRFIYDKYxAEJRT4AsBppMp7UfOm1ITgtBjy2Tbp4jUmvg17H0bmSkFd7PeibjeLTD22U7CUHsFA8omeVFDDG8LXjo9i+wiX5/c9uptiulLzS68FMOIXfuHetpN8rJgSoNJsg8YjQHEOeGBKZPHavqny8JMdxaLIZEaDYmmTc8CfQNxvF2yTMvDvF2Bq90ZCM1/s84DjgAYlGvwMLnEckSEhGNJXFxYkQDm2SZsqaCHV+SM933hwXrikJHRKAcK7IzSIdlyZCeKXXjU/d340Gm1Gy7+U4DjYjucSkgjGG//v6EFY3W/HOO1ZK+t3WQj8fxdak4ciAF0OeGD75wHrJevlEbBTblZRvnx7DygYLHpHwxSEAuKzCNRUm8ajuIfGI0ByXJoIAgN0S9bQ02kwIkfNIMg5fnwUAPL5dOvFILMym2Jp0HOn3YO/qRkndLKLziBxi0vHGgBd5nuH+jVKLR6LQR+dKCqZCSXzzxBjes7cL61vtkn63zUSChJR88XA/mu0mfOzQesm/mybjSceJIT8uT4TwHx7cUHFFwa1QbE1avnZsBB0uM96+S1qRD6D7n5QMeWI4MeTHr927FgaJr6k55xGt0+seEo8IzfGLq7NY2WBBt0QLdMF5ROKRVBy+7sbWTifWtki3gXJR55GkeKNpXJ4M46Et0r55mp+2RudJCrJ5Hv/r5QF0t9px93rpulkA2jxJzRcP9wMA/tPbtkj+3VbaPEnG6RE/jg368DsPbph7KSElNpOe3HwS8X9fG0SHy4xfuWuV5N9tNRb6qehcVcz16QhODPnx0fvWw2SQfttoNRpIkJWI7745DqOewwf2rZb8u+c6j8h5VPeQeERoitlwCscHvXjfXaskKyRtspkQokkOkuCPpXFuLIC3Seg6AuY7j0g8koZfXJkGADwssXhkMuhg1HOI00JPEr5zehwj3jj+7OltMrx5F64p6qeqnJ7pMJ67OIWPHVyHrkar5N9vNxmQyORoMl6FMMbwxcP9aHea8RsHpO37ELGaDBQxlICzYwG8ORrApx7YALNBmlHiC6Fpa9LxteMjsJn0+PB+6SZ3LcRq0tH9TwISmRx+dH4ST+9agTanNH2kC3FRYTZRgMQjQlP85OIkeAa8707p3kQ12UwIUGxNEl7t84BnwNt2dEr6vVajHnodh1iaHkqVMuKN4W9f6se93c3Y2SXdOHERm8lAUSgJCCUy+PtXBnFoY6tkUwsXYqPNk2R84YU+NFiN+PRDG2X5fqtJD54B6RxNxquEo4M+nB0L4vce2QiLUXpBAqCIjVR86bUhtNhN+NX90jskAHJeSoU7ksLPL0/jA/tWS9ofthCbyUD3Pwl4/tI0oqmc5EXZImaDDia9jjqPCBKPCO3AGMOPzk/i7nVNWCdhp0STXeg8orcalXO4x42uRit2rJRWlOA4Dg6zgZxHFZLJ8fiD71+CyaDD//7gHZIXWwJC7xE5jyrn718ZRDSVxWef2SbPeTKLk/HoXFXC0QEvjg368LsPb5yz7UsNCX2VI7qOuhqt+ODd8jgkABKPpODqZBhvDHjxW/evn3NISo3NSBFrKfiXk2PI8wwfPyh9f5jIXMSQrquyYYzhX0+NY2unE3etbZLlZ3AcB5fVgEiSrql6h8QjQjNcmghhxBuXPP/eZDMim2dU8lshiUwOxwa9eHx7hyybXafFgBiJRxXxxcP9uDoVxhfeuxsrGqSP1wDCxDVakFfGkCeG75wex4f2r8HWTundYcDCaUN0rsolzzP8zQt9WN1slS0GBdBkPCk4fN2NK5Nh/MFjm2TpZRGxGqkwu1K+9PogXBaDbA4JgGJrUhCMZ/Bvb97AEzs6sabFJtvPoftfZTDG8IUX+tA7E8FH71sny/pcxGU1UmyNIPGI0A4/Oj8Ji1GHp3etkPR7xWlT1HtUGUf6vUjneLxth7R9RyIOs4GmOFTA8UEfvnx0BB++Zw2e3CltrHAhdpMe8TQt8irhf/6yF1ajHn/0+GbZfoY4GY/OVfn89OIUemci+C9PbJWll0XESpPxKiLPM/yvw0Lx/Hv3dsn6s4TCbDpP5fLitRm81OPGRw+uh9Mij5MPEPr5DDqurgSJdC6PmXASeb5yl/2wN4b3/OMJJDN5/M5DGyQ4uqWZF/rq57oKxDM4NxaoOAKW5xn+7Lmr+PLREXzkwFpZirIX4rIY664weyacxJF+D/yxtNqHohnk8YsSRImksnn87PI0ntq5QvIFRZNNEI8C8QxWN8v39qSWCSey+B+/6MWqJiv2r5N2KpSIy2KkzqMy8cfS+MNnL2FDmx2fe/t2WX+WzUTOo0o4NujFa30e/OlTW9HqkL7UUmRuQV5HmycpSWXz+OLhfuxe1YBnJH6hcSv2OutnuTIZwuXJMCwGHcxGPSwGHSxGPVY1WdHd5ijpuwLxDD7/sx70u6P4P7+6V/Lx1LciFGbXx3maCCTwP3/Zi8sTIbQ4zGh1mNDqMKPNaUZ3mwNP7Ogoer2WyubxN7/sxbdOjWNXVwN+65B8MSgRq6k+XGK5PI8fX5jE378yiJlwCia9DqubrVjfasfaFju2djrxxM7OuVHry3Fs0ItPf/cCTHodvvepe7B7VaOsxz8/xbX2z9WYL46vHR/Bj85PIpUVOp5WNVmxfYUL21e6sGd1Iw5tbC1qgEYmx+OPnr2Ef78yg999eCP+09s2y+o6AgTnUT10HqVzebx83Y0fnpvEsUEvRD12fasdd65pwr51Tdi/vhkbSnxe1QokHhGa4OXrbkRTOVlGtjbbhQdmkEqzy4Ixhv/8o8vwRFP44X+4T7bFucNigDuSkuW7tUownsG/X5nGzy5PI5HJY2O7AxvaCv9rt6O71bFsBIMxhj/+0RWEE1l862P750QDubCb9ZgO1f7iQQ5yeR5//e+9WNNsw0cPrpP1Z5n0wpv3eB24WcS+vAs3gkjneGTE/+V5dLosePvuFTjQ3VL0vevyRAh/8bMeTIdT+LsP7JFs8udSWOtEPEpl8/hfLw/gq8dGsFQF4a6uBrxnbxfeecfKZcXVX16dwV88fw3hZBZ/9PhmvGO3vCIfUB+dR+lcHl89OoIvvT4EDhwe296BSDILTzSN6zMR+GMZ5HiGz/5Uh6d3rsCv7FuFe9e3LHmdjPri+N1/u4Ce6Qg+fnA9/utTW2R18okI56p273+MMbxwbRZ/d7gfI9449qxuxG8/0I2ZcApj/jjG/QkcH/IhleXxueev4emdK/D+fatxz/rmJc/Vt0+N4S9/fh2b2h342m/uw6om+V+41kO5+YUbQXzljRG8dH0WRp0O7967Eo9u68CwN4br0xFcn4ng5V43GANa7Ca8e28X3r9v1ZKx9mQmj09/9zxe7/fiT5/ait9+UF53mIjTYsBkIKHIz1KD6VASXzk6gp9emkIokcXKBgt+9+GN2L++BT3TYZwbD+JIvwc/vjAJANi+woX33tmFd93RJcuEO61C4hGhCX50fhIrGyw40N0i+Xc32ii2VgnfODGGl6+78blntuOO1Y2y/RynxYBhb+0u9ERS2Txe6/PgJxem8MaAB9k8w+YOBzobrDg3FsTzl6bnfq/FqMPd65pxYEML7tvQip0rXTDodQjEM7g0EcTFGyGcGRVGHv/FM9uxXeIi88WoJ+eRN5pGz3QYs+EUZiMpuCMpzIZTaLKZ8Nj2Djy4uQ12c3GP0YlAAl94oQ/97ij++dfvlH3zxHEcrHWw0Y2msviTH1/FL67OoNlugtWoF6bCFP53biyI75+dQKvDhKd3rcA796zEnWuaFt08+WNp/P9e6scPzk2g1WHGP3zoDty3oVX2v4P45j1Zw3Goq5Nh/NGzlzDoieHX7lmDTz+8ETzPkM7lkcrySGXzuDIZxk8uTuKv/v06/scve/Hg5jY8ubMT61vtWNVkRYfTAp2Ogzeaxl88fw0vXJvFrq4GfOcT98jWHXYrNpMeyWwejDHZ3/KrwbFBLz7/fA9GfHE8tbMTn31mO7oab+7P43mGy5Mh/PD8JH5+eRo/uTiFVU1WvH3XCqxstKKl4FBqdZhwdSqMzz53DUaDDl/7yD48tl2e2PtiCM+q2rz/XZoI4S+ev4Yrk2Fsanfgy79xF962SB8lYwyXJgrn6pJwrtY02/D0rhVosZtgMQruP6tRj1Mjfvzbmzfw6NZ2/MOv7oWjyGdbpdR6P9XnfnoN3z49DpfFgN95cAM+et86tLssb/l9iUwOp4b9+NH5SfzrqTF8/fgodnU14O27V8Bi0CGbZ8jkeWTzPI4OeHFxIoT/+Z5d+PA98g0IuBVhYEptPqfCiSx+/WtvYjKYxNt2dOAD+1bj4MZW6AtrhUObWvHbEK6pMX8Cb/R78NzFKfz1L3rxNy/04YFNrXjvnavw+PYO2aZ9agUSjwjVmQ2ncGzQi888vFGWN7wLY2tEaVyaCOELL/Ti8e0d+LjMTol6mLY2G07h/V8+iYlAEu1OMz563zq8e28Xtq9wzS36kpk8RnwxDHliuDQRwskhP/72xX4A/XBaDGi0GTERSAIAdBywpdOF339kIz4m8/kRsZvrY9ra0QEvPvPdC4gucO60OkzocFlwaSKEn1ycgsmgw6GNrXi8ICStaLC8ZfHui6XxpdeG8N03x6HjOPz+o5vwxA75OqkWYq9xoe/6dASf/u55TAST+NOntuKT93e/5RmSyuZxpN+Dn1+ewQ/OTuBfT42jyWbEpnYnNrQ7sKHNjo3tDoz7E/ji4X4kMnl84tB6/P6jm2TtZFlILb95z+Z5fOm1IXzp9SG0Ocz41sf348HNbYv+3n3rmvHxQ+sx4I7iJxem8NOLU3itzzP360Y9h65GKwLxDFI5Hv/1ya345P3rZY+qLcRq0oMxIJXlZXd5Ks1/+3kPvnliDGtbbPiXj92Nh7a0L/r7dDoOe9c0Ye+aJvzFM9vxUs8sfnhuEl87Prpo386+tU34P7+6Fysb5RnisBS1Wm4+G07ho988A6tRj797/x68Z2/X3Ab3Vjhu/lx97u3CuXr23AS+fHR4Ufffbz/QjT9+cuuS3ycHtXz/e/bsBL59ehwfObAW//XJrbd92WQzGfDotg48uq0DgXgGz1+awg/PTeILL/S95fc6zQb8w4f24p17Vsp5+IseYy2ep1yex+99/yImggn82yfvxd23qefgOA7rW+1Y37oeHz24HkMe4Xn13MUp/N73LuLfPnmPIi+d1ITEI0J1nrs4BZ4B77tT+sgagLm3J/UQ35CScCKLz3z3AtqdFvzdr+yR/S2r02Ks6WlroUQGH/nGmwjGs/jaR/bh4a3tiy7QrCY9dqxswI6VDXjXHUL5qzeaxukRP04O+xBJ5vDr96zFHasbsWtVg2yjjpfCZjLUfLHvd06P4/M/68Gmdgc+/44dWN1sRbvTMhchzOV5nBsP4nCPG4evz85tcBttRmzrdGHbChe2rXBiKpTEV4+OIJXj8YF9q/AHj25GZ8Nb3zjKRa1GbBhj+MHZCXz+Zz1otBnxvU/ei/3rF1/sWYx6PLlzBZ7cuQKxdA4vX5/FmyMBDHtjePHaDIILHKkHN7bgL9+xA5s6nEr9VQDMj6pO1Fi5Oc8zfPSbZ3BiyI/37O3CX75jBxpsywtymzuc+JOntuK/PLEFY/44JoNJTAYThf+bBM8Y/vCxzdjYrnzfhE08V5lcTYlHL/XM4psnxvBr96zB557ZXvSbc4tRj3fdIcQ28jxDMJGBP5aBL5aGr1Aw+/ZdKxQV+EREl1gtkecZ/ujZS0hnefz4d+4rqXPFatLj3Xu78O69XcjmBbef6PpL5/Iw6nVY22KX8egXx2YUO49qa13RMx3G556/hoMbW/D5d+woSZBrtpvwsYPr8bGD6+GPpaHjOBgNOhj1HIw6nexR6qWo1R6xL7zQh6MDXnzhvbtuKxwtxsZ2J/74ya34T2/bgjOjAdyzxFqkliDxiFAVoatiAneva8K6VnkeWiaDDia9DrEaezDJya09R8Us+CvFaTEgU1jQ1JrlM5nJ47e+dQ5jvgT+5eN3l/xWos1pxjv2rMQ7FH7LtBh2kx6JbB48z1RbwMhFnmf4n7/sxdePj+LhLW34vx++c1HrvkGvw73dLbi3uwWfe2Yb+majODsWQO9MBNenI/i3M+NzZZhP7ezEf35iiyrFijZzbYpHn3v+Gr5z+gYObWzF33/ojqKLxx1mA96zdxXes3f+RYU/lsaQJwYG4J71zapEkebfvNfWM+rbp8dxYsiPv3rXDnzkwLqS/7xex811wGmFheW+0ofs1cEfS+PPn7uKHStd+Pw7dizbs7cUeh1XiKuZsQXKCrCLYTXpa87N/OWjwzg57Mffvm93RdeFUa+DUa+DU7l3GUsiirCpGhL6IqksPv3dC2i0GfEPH9pbkZOrRcbBGqViM+qR4xkyOb7s+4TW+NF5wTX5mwfW4kP7y48A6nUcDmyolafC7SHxiFCVSxMhDHvj+NQD3bL+HLtZX3NvdeUgls7h6IAXz1+aUqTnaCFOi3A7iqZyNSUeZfM8PvNvF3DhRhD/78N3Vr2d1WY2CLGNXF5x15OcxNM5/MH3L+GVXjc+et86fPbt24p6W85xXMFpNN+5kucZRn1xAFDFHSFiM9ZebO3l62585/QNfPzgevz527dVHK9ocZhVX5yLUYZaGis+GUzgb1/sw/2bWvEb965V+3Ako9amGDLG8NmfXkMkmcN3P3FHzWwIAcHR54nUznjtSxMh/K/DA3j77hV4/z55nPpqUGuxNcYY/vOzlzEZTOIHn7pX1qmqSrOwn6oW7hUXbgTxZz+5ivs2tOCzz8g7qbiWqJ2VP1F1MMbwpdeGYDXq8bTMo5BtJkPdxdYiqSxu+BMIxDNzVvJgIgPGAJfVAJfFiAarES6rETcCCRzumcWJYT8yOR5NNiN++8Fu2XuOFiI6PGLpXM1MLWCM4U9+fBWv9Xnw1+/eKft/50ogjhWPp2tHPMrmefza197ElckQ/ts7d+A371tX0ffpdZyqopGIzayvqa63RCaHv/xZDzZ3OPCnTyvbyyEnZoMOHFc7hbGMMfz5c9fAAPzP9+yqqWLpWtvo/uzyNF64Nos/eWortnSq7xaSEptJj0SNlNDH0jn8wfcvosNlqblrai62WyPX1NeOjeLwdTc++/Zt2FdiBErrzDkvszk0QJlOQLmYDafw298+j84GC/7fh++EUYVobbVSGyt/oir5/tkJvNrnwWffvk32YlKh5Lc2FhHLkcnx+JeTo/iHVwbfUmws7rUW6bTE6mYrfuPetXh8ewf2rW1SvKNA/G8gmqqdqXhfeLEPP74wiT98bDN+vUbevs/HNnIAakPk+87pcVyaCOEfPnTHXM9ULWAz6TEZrI0FOQD8w6uDmAol8cP/cKCmFnocx8FmrJ2I4U8vTeGNAS8+/47tWN0s/6hvJbHWUMTQHUnhcz+9hjvXNOKT98vr/lYDq8lQM4LsXzx/DROBBH7w2wfQYK3uTfut6HQcLEZdTbj5zo4F8IUX+/Dkjk781qH1ah+O5NSSeP6nP7mCRDqH737iHjTZTWofTlVB4hGhCmO+OP77v1/HwY0t+PhB+W+wdnNtTgi4laMDXvzlz3sw4o3j0a3teP++1Wh1mNBkN6HZZkKD1QiOA+KZPCLJLCKpLCLJHBqsRmzucKj6NkuMrdVKafaliRC+/MYIPnzPGvz+oxvVPhzJsJvnnUe1gD+Wxv9+eQD3b2pVfHKJ3NRSuXn/bBRfPzaKD+xbVXKhZTVgrZEpNr5YGv/t59exd01jWT1HWkcUz6tdlGCM4Y9/dAWZPI8vfuCOmnHxLaRWBgY8f2kKP7kwhT94dFNN3vsAwX1U7YIszzP86U+uYlWTFX/7/t015Q4TWRhbq2Z6psN4vd+L//LEFmxWeEBGLUDiEaE4uTyPP3z2Egw6Dn/3/j2KlO7aTQbEamQTtRgTgQT++hfX8VKPG+tabPjGR/fhka0dS/5+h9kAh9mAlVB2dO7tEGNrkRoRj754uB/NdhP+7OltNbWIuNl5VP383eEBJDJ5fP4d22vqPAFibKO6F3mAsCj/7E+vwmkx4E+e2qb24ciCsNGt/mvqv/38OhLpPP72fbtrVpAAqv/N+/fPTuCNAS/+2zt3YL1Mw0rURpy2xhir2nt7OpfHX/+iF3vXNOL3Hqmdl1C3Ugsj4I8MeDDkieHvP3gHXDKnKdSiVu5//3RkGA6zoWYSAUpD4hGhOP94ZBgXbwgRkRUNyogXdrMe3mjtFCcuZNQXxzu/dBy5PMN/eWILPnH/epgN1Vc4LT5sa0HkOz3ix7FBH/786W2LTuuqZuacR1W+eACAa1NhfP+sUL68sb323j4JzqPqP08/Oj+Js2NB/O37dqO5Ru3lteCSeOW6Gz+/PI0/enwzNtXo21yxn6Wa37wnM3n8fy/24UB3S02Vmd+K1aQXhjtk+TnHRLXxiysz8EbT+OL79yheJaAktTAC/itHR7CywYK3767+bsulqIWXh2O+OH55dQafemBDzUVAlaK2djWE5rk8EcI/vDqId+5ZqWi3SK06j5KZPH7nO+eh13H4+e8ewroqfoPomJu2Vt2dR4wx/N1L/ehwmfEbB2pvYT63eKjy64kxhr/8WQ+abSb8/qOb1D4cWbCZ9MjkeWTzfNV2BAXiGfzNC724e10TfuWu2pkwdCu2Kt885fI8Pv+zHmzpcOI/PLhB7cORDVsNdB79/PI0Qoks/uCxTYo4v9XCZpw/V9UoHjHG8PXjo9jY7sD9m6p7SutyVLt4fmUyhNMjAXz27duq9llbDLYaiK19+egwDHodPn5ondqHUrXU7n/hhOZIZvL4wx9cQrvTjP/+rp2K/mybuTYiAbfyF89fQ787iv/9wTuqWjgCFkxbq/LY2pEBL86NB/G7j2yCxVh9C9blsBfEo2p3Hv3s8jTOjQfxx09uqdm3T7VgMf/CC72IpnL463fvqu2NrslQ1c+oV/s8mAol8Z/etrkmRjgvxfy0oeq8phhj+JeTY9jS4cQ962uzP0dk3iVRnefq7FgQPdMRfPzg+qqN3RWL1Vjd4vlXj43CaTbgg3evVvtQZKXa1xTuSAo/Pj+FD+xbhXanRe3DqVpq9wlPaIpcnsd//fEVjPji+OL796DBpuxmzW42VP1m91Z+cPYGfnh+Er/38EY8vKVd7cOpGJNBB7NBh2gVO1oYY/ji4X6sarLig/tqcxFhM1f/m/d4Ooe/+WUfdq9qwPvvqs3zBFR/ue9EIIEfnp/ER+9bV3NjxG/FWuVv3r9zehwrGix4ZGv1P4tuh8WoA8dV7zV14UYQ12ci+Mh9a2tfkChsdFNVKvR94/goGm1GvGdv7UwAXQqhn6861xQTgQR+eXUGH75njeyTo9VmbtpklV5TXz8+ihzP41P31647VglIPCJkJ53L4zP/dgE/uzyNP35yC+7bqLz91m4yIJMT4hu1wLWpMD73fA8ObWzFHzy2We3DkQynxYhoFTuPXuqZxbWpCP7jY7X79n3OeVTFXTr/eGQIs5EUPv+OHTXuZhH7qarzmnr23AQA4GM1OPL4Vqo5tjHmi+PYoA+/un9NTfeyAADHcYXJUNV5rr51chxOiwHvVrA2QC2sxup1SUwEEjh8fRYf3r+mKiN3pVLNhdnfODEKDsBHD65T+1Bkp5prC8KJLL57ehzv2LMSa1psah9OVVPbT3lCdRKZHD7xrXN4qceNv3hmOz79kDrTIuxm8YZXnQ+nhYSTWXz6uxfQbDPhHz5UWyN2nRZD1XYe5XmGLx4ewIY2e02/KRTfvFer88gXS+Orx0bxnr1duGttk9qHIyvV3E+Qy/N49twEHtrchq5G7UyFlItqFo++d+YG9Dqu5iMbItV6rjyRFH55dQbvv2v13JqolqnmiM23To5Bx3E12Zu4GNVamB1OZPGDsxN4556Vig0AUpNqFmS/dWoM8Uwev/MQuY4qhcQjQjYiqSw+8vUzODHkw9++bzc+ruLbY3thERGr0g2vCGMM//mHlzEdSuL//dqdaHGY1T4kSXFaqrfY/GeXpzDoieGPHt9SU4LerXAcB7vJULXOo59enEImx+PTdbCAsM25xKrvmnq93wt3JI0P7V+j9qEogs1kQLIKn0+pbB7PnpvA27Z3oMNVHx0Swka3+s7V985MIMezuhIkACBZZXGoWDqHH5ydwNO7VtSFIAFUryD73TPjSGTy+MT93WofiiLodRzMBh2SVRZbS2Ry+OaJUTy6tR1bO11qH07VU/uvHghV8MfS+M1vnkHfTBT/51f34pndK1U9nnnnUXUtIm7l2KAPL19348+e3lqTrgmH2VCVsbVcnsffvzKIbStceGpnp9qHIzvCQq/6zhNjDD84O4G9axprdpT4Qub6qapsoQcIbpZ2p7nmO3REhM6PPBhjVdVF88K1GQQTWfzaPfUhSACAzVh9EZtsnsd33xzHg5vbsL7Kh2sUS7UWZv/o3ASi6Rw+VgcxKBGrSV91gkQ6l8c3T4zh/k2t2L6yfgSJalz//eDsBIKJLLmOJKIo5xHHcc0cxz3OcVxtz4okJGEqlMQHvnwKg+4YvvqRfaoLRwBgN4vdH9X1cLqVfzwyhA6XGb953zq1D0UWnBZDVU5bOzbkw7g/gd9/ZGNNd+iIVGsB/aWJEAY9MXygRsvMb2UutlFlLrHpUBJH+j34wL7VNT32eCFWkx6MAelcdfXyfff0DaxvteO+DS1qH4piVONG96WeWXiiafzmfXUk8lVhbI3nhWl4e9c0Yu+a2ntBuBQ2o9BLmueZ2odSNM9fmoY3msanHqgP15FItfVTMcbw7VPj2Le2CfvW1faESaVYdlXGcVwTgH8HsB/A6xzHtXEc93WO405xHPfZBb9P0s+I6mTQHcWv/NNJeCJpfOvj+/GwRt4aV3N8Q+TCjSBOjwTwyfu7YTbUZoGiw2ysys6j5y9OocFqxKPbOtQ+FEWwmfRV6eJ79twErEY9ntm9Qu1DUQT73Jv36jpXz56bAM9QNx06AGArdElU0zOqbzaCc+NB/No9a+pCNBepxojNv54cx5pmGx7crI01mRJYq7Dz7fV+D8b8CXz8YO0PCVjIvNBXPfe/fzkxhq2dThxSYQiQmlRbP1XfbBQjvjjec2ftdpEqTTGv9HYD+CPG2P8A8BKARwDoGWMHAHRzHLeJ47j3SvmZHH9RQn7Ojwfx/i+fQjbP8P3fvhf3dmvnTaTDXP3i0T++PoxGmxG/WsMdIEJhdnWdo0Qmh8PX3Xh6V2fNTli7FbvJUHUTvBKZHH5+eQZP71pR8+N0RaxV+OY9zzM8e3YC929qxerm+pmIUo0Rm++cHofJoMP77lyl9qEoSrWJR9enIzgzFsBv3Lu2pvv4bqUanUffPDGGFQ0WPFkH8feFVJvQN+aL4/pMBO/ft7qqYsZSUG33v19enYGOA57YUV/XlJwsu9NhjL3BGDvNcdwDENxHTwB4tvDLhwEcAvCQxJ8RVcbr/R78+tfeRIPViJ/8zn3YsbJB7UO6iWofWd0/G8UrvW785oF1NT0lxWkxIJbJga8i6/IrvR4kMnm8c0/9vNWwm6tr8QAAv7w6i1g6V1duFnsVChJHB7yYDqdqWiRfDLGfqlriULF0Ds9dmMIzu1egyW5S+3AUxVpl5ebfPj0Gi1GH9++rL5HPYhAFieo4V55oCieGfXUV1xWpNqHvxZ5ZAMATO+rDbb4Qq7F6nEeMMfzi6gzuWd+C1hobMKQmxXYecQA+CCAIgAGYKvxSAEAHALvEn9368z/Fcdw5juPOeb3eYv9uhEI8f2kKn/zWOaxvteNH/+E+rGnR3tvieedRddzwbuWf3xiGzaTHR2u060jEaTGAseoS+X52aQqdLgv2r6+fLLXNbKg6F9+z5yawvtWOu9fVT4+ExagDx1VXFOB7Z26g1WHCY3USARWpts3T85emEM/k8ev31k+HjojNWD3ieSqbx08vTuOde1ai0VZfIp9Ox8FaRefq1V4PGAOe2lV/DolqGwH/4rVZ7F7VgFVN2tvvyI0w3KE61hQD7hhGvHE8XSdVBUpRlHjEBD4D4AqA+wCIsyMdhe+ISfzZrT//K4yxfYyxfW1tbUX/5Qj5eWPAiz969jLuWtuE7//2vWhzalPZtZmrs/sDACYCCfzs8jR+df+amn/D6zALcaJYlQgTwXgGR/q9eMeeFXUVB7BXmW151BfHmdEA3r9vVV1ZzDmOq6qNrjuSwqt9HrzvrlV1EwEVsRqr5xnFGMN3T9/AthUu7F3dqPbhKE41dX4cH/Qhmc3jHXvUH1yiBrYqKjc/3DOLNc02bKmDSaC3MhdbqwJRYiacxKWJUN3GoKqpMPsXV2fAccCTdXqu5KKYwuz/ynHcRwr/2gjgC5iPlu0BMAbgvMSfEVVA32wEn/nuBWxqd+DrH70bLg33iIhlpLEqdB595egIdBzwiftrv0DRaRE2UNXSe/TCtVnkeIZ33VE/kTVAWDxUk/Poh+cmoONQd90sgBCxqQZBAhDOU55n+NDd9RVZA6prMt6wV+j7+NDd9df3AYhv3vNgTPvx6ld63XCaDbhnvXY6KJXEUiURm2gqixNDfrxte0edXlPVE7F+6ZoQWau3XiqRahLPX7g6g/3rmjVrbKhWiilP+QqAZzmO+wSAawB+CuAox3ErATwF4F4IUbZjEn5GaBxPJIWPf/Ms7GY9vvmxu+diYVpFp+OqckKUN5rGs+cm8L47V2FFg3X5P1DlVJt49PylKWxos2PHSpfah6IoYucRY0zzC91cnsePL0zi4S3t6HBZ1D4cxamWfiqeZ/jBuQkc6G7B+la72oejOHPiURW4JF7tdQMAHtteX9FCEZtJjzzPkMnzmp58yvMMr/R68OCWtrpz8olUS7nvGwNeZPI8nqhTQaKaYrsv9sxic4cDG9ocah+KKlTLNTXojmLQE8NfvWuH2odScxRTmB1kjD3OGHuAMfZpxlgYQsn1aQAPM8bCjLGIlJ9J/ZckpCWRyeG3vnUOoWQWX//Nu6tG1LBV4YSob5wYRTbP47cf3KD2oSjCvHiUVflIlmc6lMSZsQDedUeX5gUUqbGZDMgVNk9a5+igF+5IGu/fVz9F2QuxGvVV0fV2eTKEiUCy7kp9ReanDWn/GfVqrwfbVrjQ1Vgdz36psRZcElp/+35pMgRfLI3H61TkA+ZdYlrncI8bLXYT7lxTP518C6mWaWv+WBpnRgN1HYOymQyaP08ARdbkpCy7CGMsiPkJabJ8RmiTPM/wB9+/hJ7pML76kX3Y2aWtqWq3w2Gujk2USDSVxXdOjeOpXSvq5k28OEK9GjqP/v3KNBgD3lmHXRL2BREbLb95B4Bnz06i1WHCo9va1T4UVbCbDVXRI/Fanwc6Dnhka52epyqJbQTjGZwbD+AzD29U+1BUY6FLolHDfbkvX3fDoOPw0Ob6vKYAMWKj7ftfJsfj9T4Pnt5VX92JC6kW59HL193gGfDkzvotYLaZ9MjkeeTyPAwangr4wtVZ3L22Ge116DiXG+2edUKT/M0ve/HydTc+/44deLTKpuHYqqj7AwBe6nEjms7h4wdrv+tIRIw/VkNs7flL09izuhHr6kTYW4hYQK91J18gnsErvW68Z29X3Y0+FqkWi/lrfR7ctbap7iZCiVirZPN0ZMADnqHqnv9SUi0b3Veuu7F/fTMabNrto5Sbaij3PTXiRzSdwxM76/iaqpKBAS9cE0rNt62ov1JzkWqIWA95Yuh3R/F0HU4uVIL6XE0TZXF80IevHR/FRw6sxW9W4ch4u1lfFY4WkV9enUFXoxV3rmlU+1AUQ4ytxTQuHg15ouiZjuBddeg6AqrHJXGk34Mcz+p20hBQEI807ricDafQMx3BI1vrd/NkNuig47Qf23il14M2pxm7q8h1LDXiWHEtn6sxXxyDnlhdR9aA6ij3PdwzC5tJj/s2tKp9KKpRDbG1cDKLk8M+PLWzs+6qChZSDefql1dnAABP7apfh5ickHhEFEUyk8efPXcV61vt+LOnt6l9OGVhN2v/DZRIOJnFsUFv3T2k7CYDOE77nUfPX5qGjgOe2V2fDyabWVg8aH3i2uv9XrQ6zNi5sn43ujaTAQmNx9Ze7/cAqN/IGgBwHKf5Xr5MjsfRfi8e2dIOXZ3Ga4CFk6G0e65evl4oNa9jhxggTNrV8rqP5xlevu7GQ1vaYDFqOwIuJyaDDgYdh6SG3Syv9bmRzbO6LTUXqQbn5S+vzmDf2qa6HJKiBCQeEUXx968M4EYggb95766qfcDZTYaqcR69cl14SD1dZ+KETsfBZtQjpmGnBGMMz1+axn0bWus2Sy06j7TcIZbL83ij34OHt7TV+UZX+86jV3s96Gq0YnNHfU6vEdG6S+LsWADRdK5u+8NErFUQ23i5142tnU6sbtZwKZMCCLFd7a77Lk2G4Imm8QSV+sKq8Yj1i9dm0emy4I5VjWofiqpYjeL6T5vX1Yg3hr7ZKJ4m15FskHhELMu1qTC+emwEv7p/Ne7tblH7cMrGbtb+JkpEjKztXd2o9qEojuAQ0+ZDCQCuTUVwI5DAO++o7ygUoO3Oo4sTIURSOTxcx24WQPudR6lsHieGfHhka3tduSwXw67xc/Vqrwcmgw6HNtVvvAaYv/9pVegLxDM4Nxao+8gaIEzG07Kb5XBPodR8S30/pwDhutLqNZXI5PDGgBdP7Oio65dRwIL7n0avq/nIGgmyckHiEXFbsnkef/yjK2hxmPEnT1VnXE1E65EAkUgqi2OD9Zurtpu17RA7NuQFADxcx4s9u1n7sY3X+jww6Dja6BY2TzzP1D6URTk94kcym8cjde5mAYSNrlbFI8YYXu1z4+CGlrnYVr2i9djG631CqTmJR8K5yuYZsnle7UN5C4wxHO6ZxYENLWiw1m+puYgQsdbmNfVGvxepLF/XU9ZEtH7/e+HaLO5c04gVDVa1D6VmIfGIuC1fPz6K6zMR/Pd37aj6h5vdrEc8nQNj2txEibxy3Y1Mnq+7yJqI3aztt+8nhnzY2ulEm9Os9qGohl10HmnYyfd6nwf71jXBZanu+1alaP0t4Wt9HliNehyoYlerVNhMeiQ12k817I1h3J+o6ylrIvOFsdo8Vy9fd6PDVd9dbyJz5eYavP8Ne2MY8cXxNoqsARDOlVavqRd7ZtFsN+HudU1qH4rqaPn+542m0TMdoeeUzJB4RCzJmC+O//3yAJ7Y0VETarvdbADPgHROe2+gFvLLqzNY2WCpy8gaILx90qrzKJXN4+xYEAc31rmbRePOo5lwEn2z0bp2h4mI50qLrkvGGF7r8+Dgxpaq7dKTEptJr1lB9pVeKjUXsWl42mQqm8fRQS8e20bxGkDbk6Fe6hFKzR+njS4A7Uasc3ker/V58Pi2Dhj0tG3W8v3v5LAPAHCoztfockNXAbEojDH82XNXYdLr8Ffv2qn24UiCWPKrVWECECJrRwd8eGrXirqMrAGAw2zQbBHfubEgMjm+7h9M4ttcrW50X+8TooW00RWmDQHa3DwNemKYDCbxyFbaPAHim3ftnScAeLXXje0rXFjZSFEALbtZTg37kcjkKbJWQMsRm8M9s9izuhGdDfU5eONWtFqYfWUqjGgqhwc2t6l9KJpAy9fUiSEfGqxG7Owi16WckHhELMrr/R6cHPbjj5/cUjOjDud6WjS64QUWRNbqeEqAUJitzXN0fMgHg47D/vXNah+Kquh1HKxG7U6xeb1fmN61sb2+p3cBQgwU0KbQ91qf4GZ5eCstyoHCm3cNxtaC8QzOjwfxGPVSARDufyaDTpNC3+HrbthNehzYQDFQYOFGV1vXlS+WxuXJMB6na2oOrYrnJwYFNwtdUwJadfMxxnB80If7NrRAT65LWSHxiHgLjDF88fAA1jTb8KH9a9Q+HMmwV8GEqF9encGKOo6sAcJ50qo77OSwD3euaZoTIusZu1mPuMYWDwCQzgnTux7e2la37r2FWAuOSy126bzW68H2FS4qtixgMxs0tyAHBDGWZ6AeiQVoMWLD8wyv9rrx4JY2mA0UAwUW3P80dq5ODfsBAIc2kXAuolXx/MSwDztWutBsN6l9KJpAdDNr7f435k9gOpyq+1oJJSDxiHgLL/XMomc6gj94dBOMNZTvFTf8Wo1EiZG1p3etqOuuArtGY2uhRAZXp8L0YCpgMxmQ0OB5OjMaQCKTp8haAa2Wm4cSGZy/EcSj9OZ9DptRe4IEALza60Gb04xdFAWYQ4vnqm82Ck80TV1vC9BqxObksA9OiwE7V7rUPhTNYDVpTzxPZvK4MB6idd8CDHodTHqd5oS+40PUd6QUtaMMEJLA8wz/++VBdLfZ8e69XWofjqTMxTc09nASebWXImvAfGxNa6PFTw37wRhwaBNZl4FCua8Gr6XX+7wwG3Q40E0LCGDeYq61zdMbA17keYaHSeSbQ3SzaOnel8nxODrgxaNb2+v6pcatWDU4GU8si6WN7jxWjbokTgz5cc/6FipgXoAW3XxnxwLI5Hm6pm7BatJexPD4oBddjVasbbGpfSg1D921iJv496sz6HdH8R8f21xzmVFxQoAWXS0A8Isrs3UfWQPmnRIJjZWRHh/ywWE2YPeqRrUPRRMIIp/2rqXX+z04sKFlTjSpd+Yno2jrXL3W50GL3YQ9dD3NIUZsUjnt3PsuTYQQTefwELlZbsJm0l4336lhP9a32qnUfAGi80hLQt9EIIEbgQQObqQXUQuxmfRIZvNgTDvi+YlhH4x6Dneva1L7UDSF1oS+PM9wctiPQxtbqa5AAUg8IubI5Xn8/csD2NLhxDM16H5xaDi2Fk1lcXTQi6d21ndkDVhYbK6t83RiyId71jfXVJSzErQ4VnzUF8eoL06xjQXYNeg8yuV5vDHgxYNb2mruJUUlaDFic3LYBx1HZbG3orXJULk8jzdHA3SebkGLY8XFviNys9yM1aQHY0Aqy6t9KHOcGPJh75qmuf+OCAGbxpxHVwsT8Q5uomtKCWgXRMzx00vTGPHF8YePb65JAUOLC3ORowM+ZHI8ntrVqfahqI4YL9RSafZkMIExf4IWewuwm7TnPDrSX5jeReLRHFYNThu6PBlGKJGlXqpbsGlwis3JYT92djWgwWpU+1A0hdY2T1emwoilc7iPxKOb0OJkqBPDPrQ5zdhE00BvQixiTmrEdR5KZNAzHaEOnUWwaWz9d6LQd0T3P2Ug8YgAAGTzPP7h1QHs7HLhiR21OVFFdLRoSZQQOTEsRKLqPbIGCKIEoK2C35ND4mQUWkSI2Mzacx691ufBhjY71lDmfQ5tvnkvdLNsoOtpIVo7V4lMDhdvBMnNsghCbEM7awnRzXKgm87VQsTOI62IR4wJ8Zr7NrRQvOYWtBaxFnsuKV74VrTmvDw+6MP2FS60OsxqH0pdQOIRAQD44blJTASS+KPHN9fsA81s0EGv4zTzYFrIqWE/7lnfTOWJWBAv1NB5Oj5EbwpvRWvOo0QmhzdHAuQ6ugW9joPZoNPUQu/ksB/bV7jQRKOPb8KmMZfYubEgsnmG+0jkewtWo7YmQ50c9mFrpxMttHm6CZNBB4OO00yH4qAnBm80TQ6JRdCaS+z4kA92k556LhdB7KfSAslMHufHg/RyV0Fop0ogncvjS68NYu+axpreeHEcp8melulQEqO+OO4jaywAwKaxbiqeZzgx5KMivluwmbU1be30iB+ZPE/FvougpXLzVDaPc+NB2jwtgtYm450c9sOgo7LYxbCZ9JoRJFLZPM6NBSlWvQRamgx1ci5eQ+fqVrRWLXFy2I97u1uo53IRtFSYfYYm4ikOXREEnr80jelwCv/xsdp1HYk4zAbNiBIilNW9GYfGOo/63VH44xl6MN2C3WRAJscjm9dGueWJIT/MBh320Ub3LViNeiQ0IppfGA8ik+NxH0UB3oLWNk+nhn3Yu6aRymIXQUubpws3gkjneFpDLIGWIoYnhv1Y02zD6maKVt+KlsTzKXqpe1u05Lw8MeSDSa+jlxwKQuJRncMYw1ePjmDbChceqAPLn5YWfCKnhv1osZuwpcOp9qFogrlpaxo5T6K4R7n3m9HaRvfEkA/71jXBUui4IOaxm7Vz3zs57Idex+Hudc1qH4rm0FLnRziZxdWpMA6QQ2JRrCY9MjkeeV79seKnCtfU/vV0TS2GUO6r/v0vl+dxesRPa4klEO9/yaz69z9x3Udl2YtjM+k1Uy1xfNCHO9fSSw4lIfGozjky4MWgJ4ZPPbC+5l1HgOA80oqjBRDEuxPDPhzY0FKTE+7KwWbSVmzt+JAP3W12rGiwqn0ommJe5FP/PPliafTNRikKsARWk0EzC72Twz7sXtUAp4Wmd92KlqatnRkNgGfAQXKzLIqW+qlODNE1dTusRm3E1q5NRxBN5eg5tQRaeiF1csiHVocZmzuo53IxtPIi3h9L4/oMTcRTGhKP6pyvvDGCTpcFz+xeqfahKILWxksOe+NwR9IUiVqAvbCA0EI3VSbH482RAD2YFsGmofN0ekSYNESxjcWxa6TzI5bO4fJkmM7TEmhq8zTsg8Wowx1rGtU+FE1iFV0SKp8ruqaWRysb3ZOFKZM0vXBxxMl4ap8r4aUuTcS7HVpxXp4sTJmkPZSykHhUx1ydDOPUiB8fP7Subgrh7GY9YhrY7IqIiwla+M1j0OtgNug04ZS4NBFCMpunB9Mi2DUUsTkx5IfTbMCurga1D0WTCBZz9e97Z0cDyPMMB+nN+6JYNeRmOTXsx93rmmE2UAx0MWwa2ejSNbU8Vo2Um58c8mNrp5PGiS+BVqatiRPx6KXh0mjFeXliyAenxUAT8RSmPhQDYlG+emwEDrMBH9q/Ru1DUQwtTR0ChMVEV6MVa6g88Sa0Ei88NewHxwH3ridx71ZsZu04j04O+3BPdzMMdSKCl4rNZEBSA/e9k8M+mAw63LmWii0Xw6TXQa/jVBckxBgoOSSWRisusRNDdE0th82kV/3+l8rmcXYsQJG126CVa+r4YOGlLnVTLYkWnJeMMRwb9OG+DS3QU+2HotBKu06ZDCbwi6sz+NX9q+Gqo5y8zWTQxGYXAPI8w6lCeSJZY2/GbjYgoQHx6M1RP7Z1utBgq59rpFi04jyaDCYw7k/Qovw2aMV5dHLYj7vWUKn5UnAcB5tR/YjNfAyUrqmlmHNJqFzuS9fU8thMBiRVdh6JE/GoLHtpLAbReaT2NeXD2hYbVjXRS92l0ILzctyfwFQoSQ4xFSDxqE755okxcAA+dnC92oeiKA6zXjNFzNenIwgns7RAXwSbSf14YSbH48KNIO7ppgk2i2EXnUcqb3Qp8748gvNI3fMUjGdwfSZCEd1lsJnV76cSY6A7V7pUPQ4tMz8ZT71zJV5TJEjcHosGCrNPDtFEvOXQ6ThYVRbPc3mh55LWE7dHCy6x43OTkOlcKQ2JR3VIOJnF98/cwDO7V2BlY31NkBLfQKld8gZQ39HtcJgNqot8VyZDSGV53EORtUWZ2zypfJ6EqSgmmopyG8Sxuoypd987PeIHYxQFWA6byaB6P8spioEuixY2T6cKDrED9ALqtmihMPvksA97aCLesthU7qe6PBlCNJ0jN8syaMF5eWLIh5UNFqxvtat2DPUKrQzqkO+duYF4Jo9P3N+t9qEojkND48VPDPuxqd2BdpdF7UPRHFropnpzNAAA9KZwCcTYmprOI3EqyoENrRT9vA02sx6MAekcr9oxnBz2w2bSU7HlMliNelUF2alQEmP+BAkSy6CFct+Twz7YTXrsXkWDAm6HzaRHMptXTTyPprKFiXh0TS2HVeXJoEcHfOA4eqm7HGo7L/M8w8lhPw5upLWfGpB4VGdkcjy+eWIUBze2YGcdTiYSS37VfguVyfE4OxqgB9QSCFPx1BWPTo/4saXDiWa7SdXj0Cpzk6FUPE9DhakoB+k6ui1iP4Gabr6Twz7sX99cN5M9y0Vtl8SpuRgoXVO3QwvOo5PDftzT3ULX1DJYTYJ4nsqqI56fGvYjzzMc2kTi0XLYVBaPjg/5sLurAY02WvfdDrXvfz3TYYSTWbqmVIKeOHXGL65Owx1J16XrCJh3S6gtTFy8EUQym8d9ZI1dFLvJoOqiPJvncX6c+o5uh8mgg0mvU9V5JPYd0Rvd22Mzq/uW0B1JYdgbp3HiRaD2WPGTwz602E3Y3O5U7RiqAZtRXRfzbDiFEW+cXkAVwXy5rzrn6uigF3aTHneuoYl4y2FVMbYbSWVxaSKE+ze1qfLzqwmbys5Lse+I1n7qQOJRHcEYw9ePj2JjuwMPba7Pm6Nd3ESpXMZ8ctgPHQfc200Lv8Wwmw2qCnxXp8JIZPLUd7QMgkMsq9rPPzHkw6omK9a00FSU26H2W0LRzUKj35fHbjKoNm2IMYZTw37cu6EFOhp9fFvUjq0dHfACAG10i0DtiM2xQR8ObGiByUBbruWwGfWq3f/IIVY8al9Txwd92NrpRJvTrMrPr3foTlZHnBsP4tpUBB87uK5uM6J2kzghSv1RoLu6GtBgpfLExbAXpuKp1VHw5gj1HRWDw2JAXCUhNs8znB7xk5ulCOwmdV0SJ4d9aLAasX0FTe9aDjVja6O+OGbCKXKzFIHJoINBx6nmknhjwItOl4UGBRTBfLmv8udq3B/HuD+BB+r0hW2pqHn/Oz7og40cYkUxV1ugwpoimcnj3FiQSs1VhMSjOuIbx0fRYDXivXtXqX0oqiE6j9Ts/oinc7h4I0SFpLfBbjaAV7Hg981RPza02emtxjI4zEZEU+pcSz3TYURSOZreVQRWlZ1HJ4f9ONBNbpZisKq4eXpDdLNspI1uMahV7pvL8zg26MUDm6ksthjUdF4eHRTiNeQQKw41C7OPD/lwz/pmcogVgZqxtXPjAWTyPA6SQ0w16AqpEyYCCbzUM4sP37NmbiNRj9jNovNIvdja2bEAcjyjQtLboGY3VS7P49xYkCKFReA0G1SLrZ0YoihUsdhVtJjf8CcwGUySyFckwpt3dQTZI/1edLfZKQZaJGqdq8uTIURSOTy4uV3xn12NqOmSODrgxepmK9bRNVUUajmPJgIJjPriJPIViVGvg1GvjvPy+JAPRj2H/esoGaAWJB7VCf96agwcx+EjB9aqfSiqIuZ01XQenRrxw6jnsG8t3fiWQk2H2PWZCGLpHO4h8WhZHBb1uqlODvuwucOBdqdFlZ9fTai5eXpjUHCzULFlcVhNBqSyPHhe2chuMpPHqRE/HiJBomhsKg12eKPfCx0Him0UibjuSym80c3meZwa9uP+TW3kECsS4ZpS/jklFjDfT26WorEa1XGJnRjyYe+aprl9AqE8JB7VAfF0Dt8/O4Gnd63Aigar2oejKlqIrb05EsAdqxvr2gG2HA7RIaZCn47Yd3Qv9R0ti8NsQEyF2Fo6l8fZsQAJEkUiOi7V2Oi+3ufB2hYbNrTZFf/Z1YhNpX6WUyM+ZHI8Ht5Kb96LRa3N0xsDXuxd04QGG3UmFoPVqM797+KNEGLpHB4gN0vRWE16Vbqpjg/60OEyY2M7dYgVixpCXyCeQc90BPeTcK4qJB7VAT++MIloKoePHVyn9qGojl3l7o9YOoerU2Ga4rUMcw4xFd5AnR7xY32rHe0ucrQsh8NiUKXz6MJ4CKksT8W+RTI/VlzZ+14yk8eJIR8e3tJOb96LRK1n1JF+L6xGPQ0JKAE1Ijb+WBpXpsJ4kAqYi0atzqNjg17odRxFdkvAatQjm2fI5pXru8zzDCeGfTi0kRxipWAz6RWvADk57ANjoL4jlSHxqMbheYZvnhjDHasbaYIAAINeB7NBp5rz6NxYAHmeUZ/OMqjlEMvzDGfGAriHNlBF4TQbEFXhWjrS74FRz+FeEo+KYi62pvC5OjXiQzrH45GtFIUqFmtBOFfS0cIYw+v9Hhzc2AKzgRyxxWI16RXv/Dg+JGyeSDwqHqtK5b5HB7zYu7oRLgs5xIpFDaGvZzqMUCKLBzaTIFEKapSbnxjywWk2YHdXg6I/l7gZEo9qnCMDHoz64vj4ofVqH4pmsJsNqjhaAOD0SABGPYc71zaq8vOrBceceKTsg6l3JoJoKod7ukk8KganxYBMjkc6p+x5evm6G/d2t9CivEhMBnXKLV/t9cBm0tP1VALi5knJZ9SIL46JQBIPbSGRrxRsJj2SCq8l3uj3otluwi7aPBWNGoJEIJ7BlakwFTCXiBpC37HCRLyDFIUqCTUGBhwf8uHeDS0w6Em+UBP6/36N843jY+h0WfDUzk61D0Uz2Ex6Vbp0AGEE/J5VjXOxLGJx5jZQCjsl3hwV+o4oVlgcaoh8w94YRnxxPL69Q7GfWQvYTAZFnUeMMbze58Ghja3kZikBqwob3df7PACAh7bQRrcUlC7M5nmGo4Ne3L+pFTodxWuKxWIQBQnl7n8nCg4xcrOUhk2F4Q7HBr3YtsKFVodZsZ9ZC1hNBkVFvhv+BCYCSRoUoAFIPKph+mejOD7kw0fuWwsjqbRzOMwGVWJr8XQOVybD9Ba+COZECYXfarw54sfqZitWNtZ3sXyxOArOHyVLs1+57gYAPLqNxKNSULqfoN8dxXQ4hUe3kZulFGxG5d+8H+n3YlO7A6uaaJx4KSgd27g+E4EvlqHIWonodBysRmX7qY4OeNFgNWL3qkbFfmYtYFW4ny+RyeH8eBAPUIdOydgUvqbEiXjkEFMfUhRqmK8fH4HFqMOv3r1G7UPRFGqUXALAufEg9R0Vic2svPOIL/Qd3Uuuo6IRRb5oOqvYz3yl143tK1zoIoGvJJSejPdqr+BmeZiiUCUh9r0p9eY9ns7hzGgAD1MvVckovXl6Y8ALABSFKgObgv1UjDEcG/Th0MZW6MkhVhKi8yil0Ll6cySAbJ7hEIlHJaP0XurEkA+dLgtNbtUAy4pHHMc1cBz3AsdxhzmOe47jOBPHcV/nOO4Ux3GfXfD7JP2MqAxPNIWfXpzG++9ajSa7Se3D0RR2swExFZxHp0f8MOg43LWWisuXw2zQw6jnEFMwDjXgiSKUyOIeEveKxmkRNrpKiRL+WBrnx4N4jCJrJeO0GBQV+V7v82BXVwNNLSyRuc4PhTZPJ4f9yOR5PERulpKxFcaK8zxT5Oe90e/Fzi4X2pwUrykVJV1ig54YZiMp3E+CRMko3U91bNAHk0GHu9dRIqBUrIX7nxLMTcTb1EoT8TRAMc6jXwPwvxhjbwMwC+BDAPSMsQMAujmO28Rx3Hul/EyOv2i98e1T48jyPH6LirLfgt1kULzkDRAiUXtWU99RsdjNyp6n44XSRJq0Vjyi80gpMfa1Pg94BryNxKOScVqMiCok8gXjGVy4ESQ3SxnM970psyg/0u+B3aTHPto8lYw4GS+lwMCASCqL8zeCFFkrEyXLfY+KDjE6VyWjdOfb8SEv9q9rhsVIvXylouQ1dXYsgFAiS/c/jbCseMQY+0fG2MuFf20D8OsAni38+2EAhwA8JPFnRAUkM3l85/Q4Ht/WgXWtZO+7FbvZoHhh9lzfEQkTRWM3KesQO3zdjS0dTqxupt6PYnEUnEdKiRKv9LrR6bJgx0qXIj+vlnBaDIqdpzcGvOAZ8AiJRyVjMyoXW2OM4Ui/Fwc3tsJkoBaDUlHSJXFyyIc8z/DgZrqmysFqMiCZ5RX5WUcHfdjQZqdodRmIL1eTWfnvf9OhJAbcMYqslYnNZEAqyyvivPzZ5WlYjXrqUNQIRa8WOI47AKAJwASAqcLHAQAdAOwSf3brz/4Ux3HnOI475/V6iz3kuuVHFyYRTGTxyQe61T4UTWI36xUvYj4/HkSO+o5Kwm7WI6GQyOeLpXFuLIAnaCphSYixtagCIl8qm8fRAR8e295OtuUycFmNiKaUia292udBq8OE3TROvGSUHFU96IlhKpQkh1iZKHmu3hjwwmk2YO+aRtl/Vi1iNeoUmbaWyubx5ogfD5BDoiyUFGT//co0AOCJHbTuKwebQhHrbJ7HC1dn8Nj2DkpuaISixCOO45oB/F8AHwcQAyDK6Y7Cd0j92U0wxr7CGNvHGNvX1kY35NvB8wzfOD6KPasbsY+6dRZFGFmtrPOI+o5Kx242KCbyvXLdDZ4BT+ygOFQpOM3KTVs7OexDMpvH49tpoVcOTosBkaT85ymX5/FGvwcPbWmnceJlYDLoYNBxipT7HukXSs0f2kLrqnJQaqPLGMMbBYcYTc4tD5vJoIggcWzQh3SOp3hNmSgpyD5/aRp7VjVgPSU0ykKp+9/xIR+CiSzeuWelrD+HKJ5iCrNNAH4I4E8ZY+MAzmM+WrYHwJgMnxFl8kqvG6O+OD55/3p6O78EDrMemTyPTE4ZCzMAvDkawO5VDXOTdIjlUTK29lLPLFY1WbF9BcWhSsFi1EGv4xBToIj55etCN8u93RT9LAeXxYhMnpd9is2FGyFEUjk8Sm6WsrEpVO77ep8XWzudWNFA8ZpymN88yfucGvTEMB1O4UES+cpGqcLsn16cQovdROPEy8RmVEaQGPJE0TMdwbvu6JL159QyYueb3NfVzy9Pw2Ux4IHNdE1phWJ2sr8F4E4Af85x3J8D+CaA3+A4biWApwDcC4ABOCbhZ0SZfO3YKLoarXiSbJhLItoeE5kcTAb5J9ElMjlcnghRjLBE7GY9vNG07D8nmsrixJAfHzmwlgTXEuE4TpER8DzP8GqvGw9uaYPZQMWW5eBc0E8lZznoq31uGPUc9UhUgM1kQFxm4TyayuLsWACfuJ+eS+ViNSq3edJx1CFWCTaj/GPFI6ksXu5148P715BDrEwMeh1Mep3s5+r5S8I19cyeFbL+nFpmTjyXsZ8qlc3jcI8bT+/qpLWfhiimMPufGGNNjLGHCv/7FoSS69MAHmaMhRljESk/k/ovWS9cngjhzFgAHz+0HgZ6cC2J0hOiqO+oPJRyHr3e70Umz1PfUZk4zAbZO4+uToXhiabxOE1ZK5t58Uhel9jrfR7cva4ZTotR1p9Ty9hMetlja0cHfMjxjCJrFaBEbIPnGX5yYQoHN7aiw2WR7efUOkpMhnrx6iwyOR7v3ktulkoQXGLynSvGGJ6/NI2DG1vR7qRrqlyUmIx3pN+DWDqHd1BkTVOUpTAwxoKMsWcZY7NyfUaUzlePjcBpMeCDd69W+1A0jc2s7CjQ0yN+6HUcdVCViFKdRy/1zKLVYcada+j8lIPTIr/z6OXrbuh1HB7eQm/ey8VVEHPknLg2EUhgwB0jh0SF2Mx62Z1Hz12cQofLjLvXUQy0XObfvMu3ljg7FsBUKIn33kmCRCUI09bkXfP95OIkulvt2LOKBgVUglVml9jFiRBuBBLUoVMhYsRQTuflzy5Po9VhwgF6+a4pyJ5SI0wGE3jh2iw+vH/NnLOGWBx7IbYm9+Jc5M0R6jsqB7tZ/mLzVDaPI30ePL69A3oq9y0Lh1l+h9grvW7sW9uERpv8MdNaRXQCRWR0Hh2+7gYAPLqNHGKV4LIYZRX5/LE0jvR78O47uui+VwHz5b7ynavnLk7BZtLTRKgKsZn0yOYZsnl5ui6nQkmcHgng3Xu7KP5eIXI7L5+/OAWzQYcnyW1eEfMVIPKcq1g6h1d7PXh61wpK02gMOhs1wteOjYID8NGD69Q+FM0jijhxBSauJTI5XJ4M4Z71pJqXit0kf7H5iSEf4pk8LSIqwGExyO5m6ZuNUmStQhZ2HsnFcxcnsauLptdUSoPViHBSPpHv55enkeMZ3nvnKtl+Rj0g9+Yplc3jF1dm8OTOThpRXSFyRwx/dkkY+/5uKmCuGDnLzXN5Hv9+ZQaPbeugaHWFWGUeGPDy9Vmkczw5xDQIiUc1gC+WxvfO3MB79nbR1JQiEBcRSkSiLoyHkM0zmhBVBqLIJ2dPwUs9s3BaDGSJrQCnxSir8+ilHiHN/Bi5WSpC7s6jAXcU16YieA/1fVRMg9WIiIzi0U8uTmHHShe2dDpl+xn1gNyCxCu9bkTTObx3L4l8lSLnCHjGGJ67OIm71jZhTYtN8u+vN+Tspzo+5IM/nsE77yBBolLkvv/9/PIMVjZYqFJCg5B4VAN8/fgosnkev/PQBrUPpSpwmJWLrR0f8sGg47CPeiVKRu5i81yex8vX3Xh0aztMBroVlovDLK/z6KeXprCrqwHryM1SEU6ZO49+cmEKeh1Hi3IJcMnoPBp0R3FlMkyuIwkwG3TgOPk6P567MIVOlwUHNtDLjUoRN7py9B71zkQx4I5RUbZECP1U8jjOf3ZJGPtOgwIqR07xKBjP4OiAF+/YsxI6ilZrDtoxVTnhRBbfPjWOp3etQHebQ+3DqQrEwuy4AoXZr/a6sX99M/VQlcHceZIpXnh2LIhgIktdEhXitBgQS8u30SU3izQ4zQZwHBCRQTzK8ww/vTiFBze3odVhlvz7640GqxHpHI+UDBvdn1wsiHwUBagYjuNkGwHvi6VxZMCLd+1dSb1UEmA1yhexee7iJIx6Ds/sorHvUmAzyjNtLZnJ46WeWTy9awWNfZcAOTvfXuyZRY5nNGVNo5B4VOV869QYYukcPvPwRrUPpWoQC7MTMjuPbvgTGPTEqDy2TOa6qWSyL7/UMwuzQYcH6Q1URTjMBqSyvCxFpOJGlxYQlaPTcXCYDLLEoU6P+DEbSdFEKIlwFSKGUpeb8wWR74FNrWhzksgnBYJLQvpn1M8vTyPPM4qsSYS1sO6T2iWW54Wx7w9taUeTnQY6SIEQW5NekH2l1414Jo93US+VJJj0Ouh1nCzn6meXptHdaseOlS7Jv5uoHBKPqph4OodvnBjFo1vbsW0FXWDFYjXqwXHyx9Ze6RUmDz22jcZWl4Oc8ULGGA73zOKBzW1URFohcp0nnmd4/uIU7qeNrmQ4ZSo3//GFSTjNBuqlkgiXtTAZT2Kh7/SIHzPhFEXWJESuje5PLlAvlZTIFbE5NeyHJ5omd6yEyFWY/fwlIQZ6z3qqkZACuZyXnmgKp0f9eGbPSppcqFFIPKpivnfmBkKJLD7zCLmOSkGnE254csfWXu1zY1O7A2tbqKulHOaKzWUQj65OhTEdTlFkTQIcMk3xenM0gOlwihblEuK0GCUvzE5kcnjx2izevnsFLEaKAkiBKB6Fk9JeUz++MAWn2UCTCyVEDvFoyBPF1akw3fskZD62Ju25eu6icE09spVeEkqFHNdUKJHBGwNevPMO6tCREjmEvsM9bjAGvJ1ioJqFxKMqJZXN4ytHR3DfhhZqoi8Du9kgq/MoksrizZEARdYqYN7RIr3I9+K1Weh1HLnCJMBplkc8eu7iJBxmA962nQQ+qZDDefRSzywSmTxtdCWkQQbnUSKTwwvXZvD0LhL5pESOzdNPLkxBx4HK5yVkvjBbuvtfMpPHi3RNSY4QBc2D55lk3/lKrwfZPMMzu0mQkBKbSY+ExN18L/XMYl2LDZs7qMdXq5B4VKX86PwkPNE0fpe6jsrCbjbI6jx6o9+LHM9InKgAMU4mdecRYwwvXpvFge4WNNqoo6BSxCleUk7FS2XzeOHqLJ7c2TlXykhUjstqRFTicvOfXJjCqiYr7qaJkpIxJx5J6BITRT7qpZIWh9mAqIT3vrleqs1taHdaJPveekdcT0jpaDnS7xE6dPaSyCclotCXykl3rl7tdaPDZcaurgbJvpMQrispC7PDySxODfvxxI5OiqxpGBKPqpBsnsc/vzGMO1Y30gjXMrGb9bIWZr/a60az3YS95AorG7mcRwPuGEZ8cTy1ixwtUiDG1qScuPbydTei6RzeS24WSXFaDIhIGIVyR1I4MeTDe/Z2URRAQlwWMbYm3TVFIp88NFiNkjrEKK4rD/OToaRbT7ze74HLYsB+uqYkRep+qnQuj6MDXjyytYMECYmROmL4ep8HOZ7hbVQpoWlIPKpCfnZpGpPBJH734Y10IywTm8kgqVNiIbk8j9f7vXhoSxuN2K0Ai1EHnQzF5r+8OgOOA8WhJMIhQ2ztuYtTWNFgwb3dJI5LiRBbk26j+/ylKfAMtNGVGJe1MG1NIlFiNiyIfO8lkU9ymmwmBBMZyb7vSL8HRj1H5fMSI7UgwRjDGwNe3L+pDQY9baWkROynkkroOzMaQDyTpySADFglFo9e6plFm9OMvasbJftOQnrojldl8DzDPx4ZwtZOJx6lG2HZ2GWakAIA58eDCCeztPirEI7jYJdB5Hvx2izuXtdME7wkwjnnPJLmPPliabwx4MW77qCNrtQIhdk5MCZNl8RPLkzhjtWN6G6jbgIpMRv0sBh1kjmPXrw2A54B7yaRT3IabUaEk1nJ+lmODfpw55om2M00BVRKjHodjHrpxor3zUbhjqTx4OY2Sb6PmEfqiOGrvR5YjDoc3NgqyfcR89gk7HxLZfM40u/F49s7aO2ncUg8qjJe6pnFsDeOz5DrqCLkLMx+tU94c3j/JnpQVYrdbEBCwjz1sDeGfncUT+0k15FUiM6jmETOo59fnkaeZ9TNIgNOiwE5niGV5Sv+ruvTEfTNRvE+Ok+yIMShpLmmTgz7sabZRiKfDDTaTGBMmn4qXyyN6zMRWjvIhNWoR0qict8j/V4AwINbSDySmnmXWOX3P8YYXul149DGVio1lwGbyYCERCX0xwd9SGbzNAW5CiDxqIpgjOFLrw9hfasdT9MIw4qwmwySFzGLvNLrxr3dLXNFwkT52M16STuPXrw2CwB4ksQjybCZ9OA46ZxHz12cwo6VLmzucEryfcQ8YpeOFNG1F67NQK/j8MxuKouVA5fFKInzKM8zvDnixwGKgMpCY6HcPJSo/FydHPYDAA5tIkFCDoSIjTTPqSP9Hmxb4UKHi0rNpcYiYWxtwB3DZDCJR7ZSEkAOpJw2+VLPLJwWAz2rqgASj6qIIwNe9ExH8DsPbqAunQqxmw1IyDACftQXx4g3TpE1ibCbpY2tvXBtBnvXNGJFg1Wy76x3OI4TJg5J4Dwa8sRwZTJMHToyIUYMIxKcqzdHA9ix0oUmO00slIMGqzTiUe9MBJFUjoZryESTXRCPpOg9Oj7ohctioIlQMmEzGSSJQkVTWZwfD+Ihch3JgpT9VK/0ugGAaj5kwmaUpgIkl+fxSq8bj2xth8lA0oTWoTNUJTDG8P9eG8LKBgv1FkiA3axHPCNd94fIq/SgkhS7SbrY2kQggWtTEYqsyYBTIvHo55enoeOAd+4hN4sciM6jSiM26VwelyZCNLlLRhqsRkmiUKdHBDcLiUfy0GAVxNNQhUIfYwzHB324b0MrvRyUCatRGpfEiSE/cjzDQ9R3JAuieJSUIGL4aq8bu7oayCEmEzaTHslsvuK91NmxIIKJLEXWqgQSj6qEM6MBnBsP4rcf3ECqrATYTAbwDJJ0fyzklV43tnY6sarJJun31it2sx4xiRxiL1ybAQA8tZMin1LjtBgRS1e+0T0zGsCOlQ1op4WeLIjOo0qFvquTYWRyPPavJ/FILlwSOY9ODfvR3WqnzZNMNNoEQTZcYWxtxBfHdDiFQ9R3JBtSjRV/Y8ADp9mAO9c2SXBUxK1YTdLE1nyxNC5OhOhlroxYTQYwCfZSL/XMwmTQUQF9lUAqRJXwpdeH0Oow4YN3r1b7UGoCh1l4OEkZiQonsjg7FqQHlYRIWZj9wrVZ7OxyYXUzCXtS47BUHi/M5XlcmgjhLlqQy4ZTos6jN0cDAEDOIxkRCrMrO0+5PI8zowHcS64j2WiyCc6jSmNrJ4Z8AIBDNBFKNqwmPRIVulkYYzjS78XBja0w6mkLJQfz09YqW1Mc6feCMVCNhIxIUW7OGMPL1924f2MrTZmsEujOVwVcngjh2KAPv3Wom6YFSIRUD6eFHBnwIM8zPEoPKsmQaireTDiJizdC5DqSCYfZUPG0tb7ZKJLZPL3NlRGXVRrn0dmxADa1O9BMfUey4bIYEE3nKhoB3zMdQTSdw71UQCobroKbr9LC7GODPqxqsmJtC73ckAvhOVXZeRpwxzATTlHfkYzMCRIVCn2v9rrR6bJgx0qXFIdFLIJVgn6qa1MRTIWSFFmrIkg8qgL+3+tDcFkM+PV716h9KDWDqG5LOcnrtT4PWuwm3LGqUbLvrHfsJr0k7jCasiYvjsJGtxLOjwcBgJxHMiKF8yjPM5wfC+JuiqzJistqBGOo6Lo6Veg7urebzpVcGPQ6uCwGhCpwHuXyPE4P+3H/plZwHPUdyUWT3YRghSLfkX4PAOBBEo9kw2zQgeMqi62lc3kcHfDikW3tdE3JiBTl5i/1zELHUVdsNUHikcbpn43i8HU3PnpwPY1+lxB7IbYWl8h5lOcZ3hjw4qEt7dBR2aVk2M0GpLI88hW8fQeEyNqWDic2tDkkOjJiIU4JnEfnx4PodFmwsoG6WeTCbtJDxwGRZPnnqndGcLPsp8iarLgKI+Aria6dGvZjY7sD7U66puSk0WaqqDD78mQY0XQOhzaSICEnLXYTgolMReuJNwa82NrppImtMsJxXMVTvN4cCSCeyePRrSRIyIkUsbWXemZx97pmtDjMUh0WITMkHmmcf35jGDaTHh+7b53ah1JTiLE1KSJRAHBpIohQIouHt9LiT0ocokOsggeTN5rG2bEAuY5kxGGuvPPowo0g7lrbRG8JZYTjODjMhoqcR2fHhL4jKsuWl4aCeFRuaXY2z+PsWAAHKLImO002Y0WOluODPnAccB91U8lKs90ExlC2SyyWzuHsWIBKfRXAajJUJB692uuGxajDQeoQkxWrUVijl+sSG/XFMeiJUWStyiDxSMNMh5L4+eVpfOjuNWiibglJcUgcW3u9zwu9jsP99OZQUqQQ+V7qmQVjwFO76OEkFw6LsNAr942uO5LCZDBJfUcK4LIaK+o8OjsWQFejFSsb6c27nLgslTmPrkyGkcjkcYAECdlpsJkQriC2dnzIi50rG2idJzNiR1sgXt65OjnkQzbPKLKmADaTHskyXxoyxvBKrweHNrZST6zMVBpbu3hDqCugKZPVBYlHGuYbx0fBAHz80Dq1D6XmEG94UsXWXu/34K41TWiwUbRQSubihRWIfCeHfehqtGJLh1OqwyJuQYzUlhtdu0B9R4rhtBgRKfM8McZwZjRAriMFqNR5dHqu74jEI7mpxHkUS+dw8UaINk8K0GIXYjH+MsWjIwNe2E167FtL9z+5sZnKj60NuGOYCiVpeI0CVFpu3u+OwqjnsL7VLuVhETJD4pFGCSez+N6ZG3hm9wqsaqLpG1IjOo8SEsTW3JEUeqYjeJiy1ZJjl8B5dH06gt2rGigOJSPOwvUUTZe3gTo/HoTZoMP2FTQVRW6clvJja6O+OHyxDIlHCiC+iIiUea5ODfuxtdNJE/EUoNFqLDsK9eaIHzme4X6K18hOi6N85xFjDG/0e3FwYytMBto6yY3VpEeyTEFCHL5xiK4p2bGZxdhaeWv0/tkoNrQ5YNTTNVVN0NnSKN87cwPxTB6fvL9b7UOpSWxzhdmVx9bE6RvUdyQ99go7j2LpHMb8CRIlZMZRGFddbu/R+RtB7FnVSItyBXBZDGU7j8S+o7upLFt2xBHw5TiP0rk8zo0HyHWkEA02EyKpXFmx3WODPpgNOorsKkBLQUj1x9Il/9lhr+BmeWgLvSRUgsqcR1HYTHp0UbRadmzGymJrA7NRbOmkVEC1QSt1DZLJ8fjmiVEc3NiCnV0Nah9OTWI26GHUc5KMgX+9z4sVDRaKRclApd1UvTMRAMCOLhKP5EQ8T+XE1lLZPK5NhWnzpBBOi7Fs59GbowG02E3Y0EYWc7lxmA1lT8a7PBFGKsuTeKQQTbbyI4bHh3zYv76ZulkUQOyUKie2dmpEEM7vp3ihIliN5RdmD3li2NTuoMnHCmCtoPMokspiOpzCZto7VR0kHmmQ5y9NwR1J41MPbFD7UGqaBqsJwTKz7yKZHI/jQz48tKWdYlEyMOcQK1Pkuz4tiEfbV5AIKyei8yhaxnm6NhVGNs+o70ghXBZD2YXZZ8cCuHtdM93rFIDjOLisxrIEiVPDfnAccG83OcSUoLEgHpUaXZsNpzDkiZEgoRBGvQ4ui6Gs2NqQOwqH2YBVTeRmUQJrBYXZA+4oNraTIKEEZoMOOq68aWsDs1EAwFZyHlUdJB5pDMYYvnpsBFs7nXiAFhSy0u40wxMt3b68kHPjAcTSOTxCfUey4KgwtnZ9OoJmuwkdLrOUh0XcgrMC55HYT3DnmkYpD4lYAqfFiFg6B8ZKi9jMhlOYCCRxN/UdKUaD1VhW59GpER+2dbrQaKO+IyUQ//9camn2iSEfAOAQTWlVjBaHuSzn0ZA3hg1tdhLOFcJZZrw6nMjCE01jc4dDhqMiboXjONhM5bnE+t2CeETOo+qDxCONcWTAiwF3DJ+8v5seUjLT7jLDE01V9B2v93lg0utwH41DloW5zqMynUc9M2HsWOmia0lmKuk8Oj8exPpWO1ocJPApgdNiQJ5nJS/2zhT6jvZT35FiuCylO49S2Twu3AjhAD2TFKNxbjJeaaJE70wEZoOO3rwrSLPdhECsDPHIE8OGdhIklKLNYUYgnkE2z5f05wY9giCxicQjxRDKzUtf+w3MRmGnbqqqhMQjjfGVN0bQ6bLgHXtWqn0oNU+70wxvhc6j1/u9uKe7eU7kIKRFLOOLldF5lM3zGJiNUVm2ApTbecQYw4UbQdy5hiJrSuG0CBvdUqNrZ0b9cJgN2LaCNrpK0WA1IlKieHThRhCZHI8D1HekGE2i8yhe2rka9sbQ3UbdLErSYjeVHFuLpLJwR9LYSOKRYrQX3OK+EsvNBz0xAMAmiq0pRrnl5n2zUWzudNL9rwoh8UhDXJ0M49SIHx8/tI6mDilAu9MCXyxT1oQUAJgIJDDkidH0DRnR6TjhwVSGo2XYG0Mmz2P7ShKP5MZuMoDjUHIR841AAr5YhvqOFMRZcImVGoc6OxrEnWubYKCRuorRUEbn0bmxIDgOFC9UkLnOoxLP1bA3TuXzCtPiMJUcWxsuCBIb20g8Uop2pwUA4ImUJh4NuKOwGsnNoiRWY+niEWMMA+4oDRqqUmgVqCG+cWIUDrMBH9q/Ru1DqQvaXWbkeVZWeSIAHOn3AAD1HcmM3Wwoq/NILMveQeKR7Oh0HBwmQ8mF2WLfEYlHyuGyis6j4je6oUQG/e4o9q+j86QkLqsB4RKnrQ16YljVZEVD4TwT8uOyGMFxQLiEwuxUNo+JYAIbSJBQlGa7CcFEBnwJLw2HRPGInEeK0e4UnEel9pIOeWLY1EFuPiWxmfQlF2Z7Y2kEE1nqO6pSSDzSCKlsHod7ZvGOPSvgstCiTwnaHOLDqbzeo9f6PFjXYsP6VnpzKCd2kx7xMmJrPdMRWIw6rG+lBZ8SOCyGkmNr58eDcJoN2ESLcsWYdx4Vf67Ojgki3/71FIVSElcZhdkj3hi66Z6nKDodhwarsaTC7DF/HIyBenQUptkuvDQsxdE35I3BpNdhTbNNxiMjFiLG1kpdnwuT1uiaUhKhMLu0tV8/TVqrakg80ggnhnyIZ/J4cucKtQ+lbph/OJXee5TK5nFy2E+RNQWwmw1lFWZfn45gS6cLenoDpQgOs6Hkwuzz40HsXdtEbwkVxFUQj0rpPDo3HoBJr8PuVQ1yHRaxCC6LEZkcj1S2OPGc5xlGvHF0UxRKcRqtxpJiayPeOABQbE1hWuxCP1Up0bVhTwzrWm0U2VWQVocZHFdabC2cFLqpyM2iLNYyOo9E8WgziUdVCd0JNcKL12bhtBio5FJBxEy1t8RMNQCcGvEjnePxMEXWZMduKl2UYIzh+kyEImsK4rCUdp6iqSz63VHcRWXZijJfmF38RnfQHUN3mx2WQoE9oQxi9KzY0uzZSArJbB7dFIVSnEabCaESYmtijw45l5WluSAelVJXMOSJkZtFYYx6HZptppJe7g6Jk9boXClKOYXZA+4oWh0mtNKU3aqExCMNkMvzeLnXjce2dVBRtoK0FTLV3hKnOQDAkT4PrEY97qFSUtmxm0t/ME2FkggnszRpTUEcZkNJbpZLEyEwRn1HSjMXWyuhS2fUF6duFhVwzY2AL048mnOzkCChOI02I0IlxNaGvTF0NVphM9GkViWZF4+KW/elsnncCCSoLFsF2pxmeEuIrQ26BUGWnEfKUo541D8bpfNUxZBSoQHOjAYQSmTxxI5OtQ+lrrAY9XBZDPBESu88ujQRwt41jfQmXgHKia2JZdk0aU05nCU6j86PB6HjgD2rKQqlJFajHgYdV7TzKJPjcSOQIIeECsw5j4o8VyM+YfNEPTrK02QTipiLZZjihaogOh2Kja2N+ePgqZtKFdpdlpKcRwPuGE1aUwEhHZAFY8WV0PM8w4A7RuJRFUPikQZ4qWcWFqMOD25uU/tQ6o5SH06A4BTrm41SJEohyomtXZ+JQMcB2zrpHCmF02wsKQp1bSqCDW2OuRgVoQwcx8FpKd4ldiOQQJ5ntNFVgYYynEd2k35uUhGhHA1WI8JFOo8YYxjxxsjNpwJNduGaCsSKE49o0pp6tDvNJXUeDXqEsmzqUFSWzgYLUlm+6OfUZDCJZDZPZdlVDIlHKsPzDC/1uPHg5jZYTeRiUZo2h7lk8WjYG0c6x5OrRSHsZkPJltie6QjWt9rpmlKQUqetjfho86QWTkvxQt+IV9g8UY+O8rhKjBgOe2PobnOA42jzpDRNNhOi6RyyeX7Z3+uOpBHP5MnNogJmgx5Os6Fo59GQJwaOAz2rVKDdaYYvlgbPF+doGXTHqO9IBVY1CU6vyWCyqN/fNyskA6gsu3oh8UhlLk+GMBtJ4cmdFFlTg3aXueRRoNdnwgCAHSspbqMEDrMe8UyuaEssIMTWttP5URSH2YB4Jo98EQu9bJ7HDX8CG9rJzaIGpTiPRnxCjw7F1pSnHOcROcTUodFW/LkaLgiyNGlNHZodppLEo1VNVqooUIF2pxk5niFQRBw0kspiNpLCJopCKc7KQkxwKlSceDTgLkxao3NVtRQlHnEc18Fx3LEF//51juNOcRz3Wbk+qxde7JmFQcfhka0dah9KXSLaYksRJnqmIjAbdOimzZQi2MwGMAYkixxXHU5kMRVKUqxQYcQi5nhmeVHiRiCBHM/Q3UpvCdXAaTEU3aMz6o2j1WGaEzII5SilMDuVzWM6nKRrSiVE8aiY0ux58YjOlRo0201FF2YPe+NUlq0S7S5hInIx0TWxLJucR8ojdkxNFyke9c1GsarJCoeZhgVUK8uKRxzHNQH4FgB74d/fC0DPGDsAoJvjuE1SfybPX1V7MMbw0rVZ3LexlRbmKtHutCCd4xEpIW7TMx3B1k4nDHoy7imBvfCAKbb3qKfgDKNJa8oiLgSKia6JU6HIJaEOLouxBOdRjAQJlTDqdbCZ9IgUIR6N+uJgjK4ptWi0CVO8QkW4JIY9MTjMBuqmUokWuwn+IjqP8rzQTUV9R+ogXh/FpAOGPORmUYtmuwkWow5TRcbWBtxRbKHzVNUUs/vNA/gggEjh3x8C8Gzhnw8DOCTDZ3VBvzuKMX8CT9KUNdVodwkPJ2+RvUeMMVyfoUiUktgLvUXxdHHOI5q0pg4OS/EiH/XoqIuzFPGIolCq4rIYi3IekSCrLk0lOY/i2NBmp24qlWixmxEoIrY2FUwineNJPFKJdmfBeVTE+nzAHYPFqJvr3yGUg+M4rGy0FhVby+R4jHjj1HdU5SwrHjHGIoyx8IKP7ACmCv8cANAhw2c3wXHcpziOO8dx3Dmv11vc36wKePHaLDgOeHw7RdbUoq2ENxuAkOkNJ7MUiVIQ0XkUL9J5dH0mgg6XeW4kL6EMovOoGFFihKJQqlJsbC2cyMIfz5AgoSINVmNR50oUZKmbSh0arYLzKFiM84gmralKs8OEYCKzbF3BkFdws5B4pA6lvNwd9MRo0pqKdDVai4qtjfhiyPGMJq1VOeXkbmIARGnXUfgOqT+7CcbYVxhj+xhj+9raamec/YvXZrFvbdOcgEEoj/hmo1jnUQ+5WhTHUap4NB2hyJoKOEtwHolToQh1cFkMiKVzy06xGfYVHGIUW1MNl9VQnPPIF8fKBgtsJuqRUIOGIguz4+kcZsIpmrSmIi12E7J5tmxdwZBHuP9tbKONrhpYjHo4LQZ4IkXE1txRbGqn86QWq5qKcx71z1K8sBYoRzw6j/lo2R4AYzJ8VvOM++Pom43iCYqsqcqc86iIQj5AECZ0HLCtk8QJpbCJsbUiiphT2TyGPDES91TAaRE2T8WMgB/xxWnSkIo4LUYwBsSWuaZGC1Go9XSuVKPBakQkWVwUlARZ9XBZDNDruGWdR6OF6YV0/1OPZrvgElsuujbkiaHVYZ4TBgnlaXeal42tRVNZTIdT2NRB9z+1WNlghS+WQWqZwTYD7ij0Oo7czFVOOa+ofgrgGMdxKwE8BeBeAEziz2qel3pmAYDEI5VxWQwwG3RFx9Z6piPobnPAaqKxrUox7zxavvNoyCNYYndQJ5XiFFuYHUpkEPj/t3fvsZFd9R3Av2fmzvvp13jXs48km80+7LxNGiioGxoIVEVQaAkFSqVW/SNFFVVVqar4o1VVIfWP9o9GAgmJVhQKhbb/gCpQ0yzbtCUJuxsC+7I369mnvWOPn/OwPeOZOf3j3ju2x/d6TJT1Ob7z/fwT27N/nOjozLn3d36/36nUmM2iUDKyXmKYDLu/GOVmyzB8Aod6o7s1NGqTjARw5W5p238jpcREoYKPP5HdpVFROyEEUpFAx55HE+z3ppwdPJorV7ct87w2U8aDGb7kqpRJhDsGj+wMMWYeqZPtWb9xbbvvtvF8CQ/0xxAy+A61l+0480hKecr6bxFmk+vXADwjpVx6p//2jvyfae6HF/MYySZxkA/lSgkhkEl2PtmwXZ5aYknULvtFeh61mmVzjnbdThtmT7Cxr3I7zRLLFSo41BtFgDdLKpMMd+55VChVUa7W8QD7HSmVju4geDRThk8Ah/v47KdKX8zMOJ/bJvNISmkFjxjkU8l8Pt/+cPetaTN49BAzj5TJps3gUafStfHpEptle8DbKo6XUi5g/Ya0e/I3L5NS4qOPZZFmKqwWMonwjnoeLVRqmFpaZbPsXRazenhUap0zjy5NLSEeMpgpoYA9T50aZvOmNfXs/lSd54o3ramWipg34zWaEn6XZrDrAVmuKZXSkQAWV7YvhZqwArI8eVenN965bK1QrqK4WseDXFNKZRIhzBSrkFK63k741kwJIcOHAz187lNlyA4eLbgHjyrVOm7Pr+CTTx7crWHRPcLjRAWEEPjd99yHjz7GFHMd7KSmGjBv8QLAkqhdFg1ZPY92kHl05W4Jx/cleOOGAn6fQCzo31HmUcAvcJBX6iqzk8yjZlPi+lyFt3cplrRuJNyuHDRnNTZnE2a1eqLBHZWt8aY1tfp20PPILoXimlIrkwijWm9u29z86rSZIeYWXKd7b18qDJ/AtjeuXZ22mmUz82jPY/CIup55stG559GlKbOiks2Yd1fA70PQ8HUMHkkpMZYv4hg3JmXiYaNjz6NcoYzDfTEYLIVSJrmDzKPJxRXU6k1msyiWinS+xStXqCAc8GF/MrxbwyIHqQ5la42mxPXZCgMSioUDfsSCfsyV3YNHE/ZNa5wrpTJJs8SwsE3p2rWZMo5ynpQK+H3Ylwzjzg6CR8d409qex6d36noDiRCKq/WOtwRcnipifyrcarZIuyceMlDqEDzKF1dRXK3jOINHysRDRsfMo9xshb1ZFLMzj4rbBSSsW6E4V2rZgb7tg0dl3N8fZ8alYmbmkXtAYmpxBdV6k2tKA73xIOYr7hnn12bKiIcM7GNAVqlONyKXq3VMLq7gKAMSyg2lI9uWrV25W0Ik4GdbCQ9g8Ii6XiZhPhx06nt0aarIfkeKHOyJtK4NdzOWt0419nGOVEmEA9sG+eqNJm7OVZjNopjd82i7UgD2ptKDnXm0XdPs3Cx7U+kgHQmgUmugVm86fn6twFIoXfTGQts2zL5WKOPIQMy1zw7tDvv53K21xPpNa1xTqmV7Iphacg8ejefNZtk85Nj7GDyirjdgpcVud6PDSq2BiUKZt3gpMpxN4dLUEqSUrv9mPM+UWNUSYWPbPjp3Flaw1pB80VUsHPAj6PdtW7aWK1SQCBvojzPTUqVUdPuytWq9gdvzyzjCbBbl7EtQ3Jpm26VQ7HmkXl8suG3Z2rWZMoN8Gsh0eD63S6GYeaReNh3B3cVVNJpbn9PtthInWBngCQweUdfLJOyaavfMo/HpEpoSOMlm2UoMDyVRXK3jzjYpseP5Evanwq2XLdp98dD2PY8mCnx50kWnQN91q7yQJ+9qJTuUGN6aW0ZTMkNMB+moGWh163uUm62gJxpg6bsGemNB14bZxdU1TBer7HekgUTIQDjgcy1buzxVRCzox2GWQik3lI6g3pSO71IzpSoWltfYVsIjGDyirtcpLRZYb5bNsjU1RqygnT0PTsbyJTzE0yelOvU8ylmlh0eYeaRc0roC3k2uUGZAQgOdGmZPWGuK2XzqtTKPXIJHEzO8aU0XfVbwyCmbudUsm3OlnBACmUTY9fn8wuQShodSLIXSQNa6QXdycXnLZ2wr4S0MHlHX640F4fcJ15MNwOx3lAwbOMDrxZU4ti8Bv0/g0lTR8fO1RhMTM2WeaijW6ba13GwZvbFg64Se1EmEDdc+Osu1OqaWVtnYVwPRoB9+n3Cdq9ys+aJ7P+dKuZ5W5pFL2VqhwuCRJvriQdQaTcfDjmu8aU0rmUTIsWyt0ZS4PFXEcJYBCR0cSJvvR04VAmN3zWd3PqN7A4NH1PX8PoG+WHDbnkeXp4o4OZRkCYci4YAfDw7EcXHSOfPoxmwFtUYTx7gxKZUIGSjX6mg61LwD5ssTAxJ6MMvWnAN91+2b1viiq5wQAqlIwDXzKFeoIJMItW7QI3XsLDGnzKOl5TXMlqvMENNEb8xsV+BUunb5bhHhgI+3Qmkikww5Zh5NFMpYWWvg4SzbSehgyAoeTS1ufZcaz5cwmAyhhyW7nsDgERHcNyfAPN0YyxcxzH5HSg1nk66ZR+PTdkosg0cqxcMGpASW1xqOn+cKvBVKF4lQwLXnkV1eyGwWPSTDBoorzoG+iUKZa0oT9ouRU8PsiVn2e9NJnzVXTjeunbuxgMcOpmH4+Yqkg0wijIJDZcCFO+ZhIoNHeoiFDKSjAceytSv5Eo6zZM0z+M1IBHNzcitbyxXKWF1r8qY1xYaHUpgpVR0zxMbzJfh9gmnmitnZD06la0sr5sk7X570sJPMIwaP9OCWeSSltAKyXFM6iAX9MHwCCw6ZR61+b9yjtGA3LZ9vu3GtUq3j8t0i3nVfr4phkYOBRAilah0rtc2HUhcmlxAN+vn9p5FsOoLJtrK1tUYT12ZKOL6fh7teweAREcya6kLZ5TYHq1aXddVqjVjNyp2yj8byJdzfH0PI8O/2sGiDeMgAAJSrTi9P5sk7H/T0sF3D7FyhjGw6gkiQ60kHSZfg0XylhqWVNZaCakIIgXQ06Fi2Np4vIuAXOMi+iVrobWUebX7ue/P2IhpNiVEGj7ThdiPyxcklnNyfhJ/NsrUxlI5sKVu7PlvBWkOy35GHMHhEBHNzmitX0XDo1XJpqoig4WPGhGInreDRZYfg0Xi+xJI1DcTDZvCo6BCUyPFWKK0kwubNeE7feblZlhfqJBkJODbMzs0ym0U36WjAsWH2K1dnMXq4l6VQmuiLO5etnb0xDyGAxw+lFYyKnGSS9o3I60GJRlPi0lQRIyxZ00o2HcHk4sqmWwyvtJpl8wDeK7iLEQEYSIbRlMCcQ/bRz+8s4thgAgE+9CmVCAdwuC+KS1Obm2ZXqnXcml/G8UEGj1RL2JlHTsGj2TIMn2ATUk24lRi2SqGYzaKNVCSAokPmkZ3Nd6SfwSNdpCOBLZlHk4srGJ8u4f3HM4pGRe2iQQORgH9L2dq5Gws4vi+JJBvQa8POPNrYlzTHZtlaOtATQbla39SjbzxfguETPID3EL4NEwEYiG/dnABgoVLD2RsLeO/RfhXDojYjQylcnNyceXSVzbK1YWceOV1/nCtUcKg3yiCsJhKtLLHNL7qFUhXlap39jjSSDAdQXKlvOs0FzNsLg4YPWZZCaSMdDWKhLfPo9NgMAOAZBo+00hsLbrptrd5o4qe3FvCu+3oUjoratYJHxfXMowvWzbsPH2DwSCf2jWuTi+t9j8byJRwZiCNo8NnPKziTRDBvWwOwpRnzS1em0WhKfHhkn4phUZuTQ0ncml/e9MI7njeDR0yJVS++TeYRb4XSS9IKHrX3PbJLodibSh+pSAC1RhOra83W35pNiR9ezOPxg2n2/NBIOrq1P9XpK9M43BfFEX7/aaUvHtxUtjaWL6FSa7DfkWZ6okEYPrHpcPfC5BIiAT+zWTSTdQoe3S2yWbbHMHhEhI0nG5szj354MY9sOsLUWE0MO/Q9GsuXEA36cYCn78olQmaqf6kt86jRlLgxt8wHPY3YZRmltswj9qbSTzKyNUvsv68WcGt+GZ99+rCqYZGDnujmsrWVWgM/npjDM8cyEIJBPp20Zx6dvTEPABg9zMwjnfh8AgOJ0Kbg0cXJJZwcYrNs3dhZsJMLywDMW3anllZZGeAxDB4RwbwKFNhctlZaXcP/vjWLD43s40OfJoaHzCDexcn1vkfj+RKODibg40OEcrGQeTtXe+bR5MIKavUmAxIasXsetTc3zxXKCBk+DKUYjNVFKmLO1caMln969QYGEiE8N8ysWJ2ko0GsrDWwumZeK/5qbhbVehO/eoIla7ppDx6du7GAbDrSKr0hfWQ2BI/sZtk81NVPXyyIkOHD1JJZxWFXBpxgZYCnMHhEBCBk+JGOBjZdBXp6bAa1RpMlaxoZSIQwmAy1Mo+klBifLrFZtiYMvw/RoB/l6uZslolZs7EvS6H0kWiVra3P1UxpFd//+RRO7E8yGKsRO0vMbpp9c66CM1cL+PRTh9hHQjPp6OZA3+mxGUSDfjx1P0uhdNMXC2K2XIWUElJKnLs5j1H2O9LSQCLc6nl0fbaM5VqjlYlO+hBCmDeuLZhla+N581mdmUfewqcOIot5srHe8+gHF/LIJEJ44hAfJnQyMpTCRevGtUK5ivlKjRuTRuIhY2sfHbsUik2YtZFo63lUqzfxwjffwNLKGr70Gw+rHBq1ac88+uZrN+EXAp/+pUMqh0UO0hHzCviF5RqklDh9ZQbvfbAfIcOveGTUrjcWQrXexHKtgTsLK5guVtnvSFOZZKh1uMtm2XrL9kRwx+p5dCVfQjJsYH8qrHhU9E4yVA+ASBeZRLiVFrtcq+PM1Rl8cvQgT+A1MzyUxJmrBayuNTY0y2bwSBfpaADfOXcbr+bm8NBgAg8NxvHTW4tIRQLojQVVD48sibaeR3/5/Us4f3MBL/724zjJE12tbAwerdQa+M7Z23huZB8Gk3wg102PlXm0uLyG8ekSppZW8YVnjyoeFTnpi5v70Xylxn5HmsskQpir1LDWaOLCnSLCAR8eZCazloZSEYzlzRsmzWbZSbb+8BgGj4gsA4kQrl83MyTOjBewutbEh1iypp2TQyk0mhJj+VIreMTMI3387W89hpfHpnF1uoSr02X8aGwG9abEux/o4wOERoKGD+GAD6XVOv759Zv41uu38MKpI/jIo0Oqh0ZtkpH1srXv/WwSxdU6PsdG2VpKtYJHNbxxawEA8Mwx9jvSUZ91mDFXqeHsjQUkwgYeYgm8ljIJM1A+W67i4uQSTuxPwvCzeEZH2Z4ICqUqVtcauDpdxsefyKoeEr3DGDwismQSZlqslBI/uJhHbyyIp5jCrJ2RrJkVcXFyCWP5EvrjIfTFQ4pHRbaHD6Q2pZPX6k1cn620mtKTPhLhAH48MYd/+L/r+JWHBvCnHzymekjkIGmVGC6t1PHdc3dwbDDBHjqaSkfNgMTi8hpOX5nBSDaJDDPEtGRnws5Xqjh/cx5PHu7h7V2asm9Ezi+t4tLUEj7x5AHFIyI3Wavh/Nkb8yhX6zjOZtmew7AtkWUgEUKt0UShVMXpK9P44MlBnmxoKJuOIBUJ4NJUEeP5EkvWNBc0fDi2L8GSNQ0lwgYuTC4hm47g7z/1OF+cNGX4fYgF/fjR+Awu3y3ic+85zCw+Tdlla9fnKnjj1gLef3xQ8YjITV/MDEjkChVcnS7jXTws1FYmac7VT67Po1JrYIQ3rWnLvq3w5Stm6RorA7yHb8ZEFvt08N/fmESl1mDJmqaEEBgeSuLC5CLemilxYyJ6m3qiQcSCfnz1c6OtchvSUyoSwJu3F5EIGfjYYywD0FUk4EfQ78P335xCUwLvP86SNV31Wj2PXro8DQB4kv2OtGWXrb08ZgYkHmbwSFsHeqzg0Zi5rviM7j0sWyOy2Gmx33ztJhJhA+850q94RORmJJvCV1/JAeDGRPR2/cVHTgIA+3zsAclIAFNLq/jN0QOIhfjopishBNJRc67640E8wpdcbcWCfgQNH87emEfAL/DogbTqIZGL/ngQQgDnby4gZPhwNMNm2bralwpDCOD2/AoO9UYR537lOcw8IrLYwaPJxRV84OQgggaXh66GN9wGxbI1orfnkQNpPMIXpj3Bbpr9O2yUrb20lcV36liGt7VqTAiBvlgQTWkeSEWCftVDIheG34e+WBCNpmSzbM0F/D4MWpliPNz1Jq4+IsvGhr4fHtmvcCTUyfCQeZorBHA0w82JiLztl4/04/nRg3iA11Nrz26azZI1/fVZpWvsd6S/ASsgwZI1/WWt0rUTDB55EnPJiCzxkIFIwA+fAN53lCVrOru/P4ZIwI/BZIinhUTkeV949qjqIdAOpSMBGD7B54g9oNdqms1+R/rLJEK4cnf9xl3SVzYdwfmbCzi+n3PlRQweEVmEEDg6GMexwQTCAQYkdOb3CTxzfAD9cV7/TkRE+vjEkwfw+KEeJMJsQq+7PusW0FEGj7Rnt5bgTWv6szOPWLbmTQweEW3w7T94GoafPQr2gi9/5knVQyAiItrkuWHe1LpXPHtiECHDhz4eRGnvwUwcPdEAL3jYA54b3oeZYhX39cVUD4XuASGlVD2GX8jo6Kg8d+6c6mEQERERERHRPbbWaKJSrbd6ihHRvSOEOC+lHHX6jA2ziYiIiIiISEsBv4+BIyINMHhERERERERERESuGDwiIiIiIiIiIiJXDB4REREREREREZErBo+IiIiIiIiIiMgVg0dEREREREREROSKwSMiIiIiIiIiInLF4BEREREREREREbli8IiIiIiIiIiIiFwxeERERERERERERK4YPCIiIiIiIiIiIldCSql6DL8QIUQBwE3V43iH9AOYVT0IUoJz3904/92Lc9/dOP/di3Pf3Tj/3Ytz39324vwfllIOOH2w54JHXiKEOCelHFU9Dtp9nPvuxvnvXpz77sb5716c++7G+e9enPvu5rX5Z9kaERERERERERG5YvCIiIiIiIiIiIhcMXik1ldVD4CU4dx3N85/9+LcdzfOf/fi3Hc3zn/34tx3N0/NP3seERERERERERGRK2YeERERERERERGRK0P1AIi6gRAiBeBfAPgBVAA8D+AagJz1T/5ISnlB0fCI6B4RQrwAc70DQBrAeQAfANc+kWcJIQYB/JuU8n0u+38T5ncAvweIPKJt3bfv/a8D+Dy47mmPY9maIkKIrwE4CeA/pJR/rXo8dG8JIf4QwFtSypeEEF8BcBdATEr5Z4qHRveYEMJA28MCgD8G13/XEUK8COAbAD7Bte9tG18irN+37Pl8DvAmIUQPgG8DyEgpn3DY/38A4A6A5/k94D1tAYQt+7+U8gLXvve0r/u2z14E8HWYQWOue49xOSD4Cjy657NsTQEhxMcB+KWU7wbwgBDiqOox0b0lpfyylPIl69cBAHUAvy6E+IkQ4mvWAwZ50yMAvi2lPCWlPAXgKLj+u44QIgtgEMAouPY9zXqJ+DqAmPX7lj2fzwGe1oD58lAEHPf/GQBPg98DntO+9tG2/1uBI659b9q07m323i+lPAeue6/6DIC/k1J+EEAewKfg4T2fwSM1TgH4rvXzfwJ4r7qh0G4SQrwbQA+AlwA8K6V8CkAAwK8pHRjdS5seFgA8C67/bvR5mCdRZ8G173XtLxGnsHXNO/2NPEBKWZRSLrX/3d7/pZSvgd8DXtW+9p2CBafAte85buse63s/wHXvSQ4HBJ+Fh/d8Bo/UiAGYtH6eh3kaTR4nhOgF8CKA3wPwcynlXeujczCzUcib2h8WPgyu/64ihPABeAbAGXDte57DS4TTns/ngC7Stv8D/B7wJIe17xQs4NrvEm17P8B172kbEgRuw8N7PoNHapQBRKyf4+A8eJ4QIgjgXwH8uZTyJoBvCCEeFUL4AXwMwM9Ujo/uqfaHhX5w/Xeb9wF4XZpNBrn2u4/Tns/ngC7hsP8D/B7oFk7BAq797rFx7we47j2r7YDA03v+nh34Hnce6+lqjwK4oW4otEt+H8ATAL4ohDgD4BLMxrlvAnhVSvlf6oZG91j7w8LnwfXfbZ4D8Ir181+Ba7/bOO35fA7oHpv2fyHE8+D3QLdwChZw7XePjXs/wHXvSQ4HBJ7e83nbmgJCiCSA/wHwMswSlqdd6mSJaI8TQowA+BYAAeB7AP4GXP9EnieEOCOlPOW05wOQ7X/j9wCRN2xY+5v2fynlF/kOQOQtQogXAHwJ65lk/wjgT+DRPZ/BI0WsGxk+AOAVKWVe9XiIaPdw/RN1F6c1z+8Bou7EtU/kbV7e8xk8IiIiIiIiIiIiV+x5RERERERERERErhg8IiIiIiIiIiIiVwweERERERERERGRKwaPiIiIiIiIiIjIFYNHRERERERERETk6v8BsZ4Z9XiDDjUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,5 ))\n",
    "plt.plot(prediction_real_change)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
